{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %matplotlib widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scaling_tables import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read raw data - one row per iteration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The main variables used here:**\n",
    "\n",
    "- dense_all - all dense experiments, each row is one iteration\n",
    "- sparse_all - all sparse experiments, each row is one iteration\n",
    "- sparse_wts - experiments with weight sparsity only, each row is one iteration\n",
    "- sparse_activations - experiments with both weight and activation sparsity, each row is one iteration\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_all = pd.read_pickle(\"scaling_results/sparse_scaling.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "dense_all = pd.read_pickle(\"scaling_results/dense_scaling_baselines.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_activations = sparse_all[sparse_all[\"Activation sparsity\"] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_wts = sparse_all[sparse_all[\"Activation sparsity\"] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Accuracy</th>\n",
       "      <th>Iteration</th>\n",
       "      <th>Best accuracy</th>\n",
       "      <th>L2 dimensionality</th>\n",
       "      <th>L3 dimensionality</th>\n",
       "      <th>Seed</th>\n",
       "      <th>ID</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Sparse_Baselines</td>\n",
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       "      <td>1600</td>\n",
       "      <td>3200</td>\n",
       "      <td>5882</td>\n",
       "      <td>Sparse_Baselines cnn_out_channels=(64 128)cnn_...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sparse_Baselines</td>\n",
       "      <td>64</td>\n",
       "      <td>128</td>\n",
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       "      <td>63.597179</td>\n",
       "      <td>1</td>\n",
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       "      <td>1600</td>\n",
       "      <td>3200</td>\n",
       "      <td>5882</td>\n",
       "      <td>Sparse_Baselines cnn_out_channels=(64 128)cnn_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sparse_Baselines</td>\n",
       "      <td>64</td>\n",
       "      <td>128</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0</td>\n",
       "      <td>32852</td>\n",
       "      <td>67.633229</td>\n",
       "      <td>2</td>\n",
       "      <td>95.101881</td>\n",
       "      <td>1600</td>\n",
       "      <td>3200</td>\n",
       "      <td>5882</td>\n",
       "      <td>Sparse_Baselines cnn_out_channels=(64 128)cnn_...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sparse_Baselines</td>\n",
       "      <td>64</td>\n",
       "      <td>128</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0</td>\n",
       "      <td>32852</td>\n",
       "      <td>52.664577</td>\n",
       "      <td>3</td>\n",
       "      <td>95.101881</td>\n",
       "      <td>1600</td>\n",
       "      <td>3200</td>\n",
       "      <td>5882</td>\n",
       "      <td>Sparse_Baselines cnn_out_channels=(64 128)cnn_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sparse_Baselines</td>\n",
       "      <td>64</td>\n",
       "      <td>128</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0</td>\n",
       "      <td>32852</td>\n",
       "      <td>19.592476</td>\n",
       "      <td>4</td>\n",
       "      <td>95.101881</td>\n",
       "      <td>1600</td>\n",
       "      <td>3200</td>\n",
       "      <td>5882</td>\n",
       "      <td>Sparse_Baselines cnn_out_channels=(64 128)cnn_...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Experiment name L1 channels L2 channels  L3 N  L1 Wt sparsity  \\\n",
       "0  Sparse_Baselines          64         128  1000             0.0   \n",
       "1  Sparse_Baselines          64         128  1000             0.0   \n",
       "2  Sparse_Baselines          64         128  1000             0.0   \n",
       "3  Sparse_Baselines          64         128  1000             0.0   \n",
       "4  Sparse_Baselines          64         128  1000             0.0   \n",
       "\n",
       "   L2 Wt sparsity  L3 Wt sparsity Activation sparsity Non-zero params  \\\n",
       "0            0.99           0.995                   0           32852   \n",
       "1            0.99           0.995                   0           32852   \n",
       "2            0.99           0.995                   0           32852   \n",
       "3            0.99           0.995                   0           32852   \n",
       "4            0.99           0.995                   0           32852   \n",
       "\n",
       "    Accuracy Iteration  Best accuracy L2 dimensionality L3 dimensionality  \\\n",
       "0  74.098746         0      95.101881              1600              3200   \n",
       "1  63.597179         1      95.101881              1600              3200   \n",
       "2  67.633229         2      95.101881              1600              3200   \n",
       "3  52.664577         3      95.101881              1600              3200   \n",
       "4  19.592476         4      95.101881              1600              3200   \n",
       "\n",
       "   Seed                                                 ID  \n",
       "0  5882  Sparse_Baselines cnn_out_channels=(64 128)cnn_...  \n",
       "1  5882  Sparse_Baselines cnn_out_channels=(64 128)cnn_...  \n",
       "2  5882  Sparse_Baselines cnn_out_channels=(64 128)cnn_...  \n",
       "3  5882  Sparse_Baselines cnn_out_channels=(64 128)cnn_...  \n",
       "4  5882  Sparse_Baselines cnn_out_channels=(64 128)cnn_...  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>L3 dimensionality</th>\n",
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       "      <td>800</td>\n",
       "      <td>1031</td>\n",
       "      <td>Dense_Baselines cnn_out_channels=(32 32)linear...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dense_Baselines</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>800</td>\n",
       "      <td>1031</td>\n",
       "      <td>Dense_Baselines cnn_out_channels=(32 32)linear...</td>\n",
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       "      <th>3</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>839476</td>\n",
       "      <td>52.390282</td>\n",
       "      <td>3</td>\n",
       "      <td>95.924765</td>\n",
       "      <td>800</td>\n",
       "      <td>800</td>\n",
       "      <td>1031</td>\n",
       "      <td>Dense_Baselines cnn_out_channels=(32 32)linear...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dense_Baselines</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>839476</td>\n",
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       "      <td>4</td>\n",
       "      <td>95.924765</td>\n",
       "      <td>800</td>\n",
       "      <td>800</td>\n",
       "      <td>1031</td>\n",
       "      <td>Dense_Baselines cnn_out_channels=(32 32)linear...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Experiment name L1 channels L2 channels  L3 N  L1 Wt sparsity  \\\n",
       "0  Dense_Baselines          32          32  1000             0.0   \n",
       "1  Dense_Baselines          32          32  1000             0.0   \n",
       "2  Dense_Baselines          32          32  1000             0.0   \n",
       "3  Dense_Baselines          32          32  1000             0.0   \n",
       "4  Dense_Baselines          32          32  1000             0.0   \n",
       "\n",
       "   L2 Wt sparsity  L3 Wt sparsity Non-zero params   Accuracy Iteration  \\\n",
       "0             0.0             0.0          839476   9.796238         0   \n",
       "1             0.0             0.0          839476  85.031348         1   \n",
       "2             0.0             0.0          839476  26.410658         2   \n",
       "3             0.0             0.0          839476  52.390282         3   \n",
       "4             0.0             0.0          839476  89.811912         4   \n",
       "\n",
       "   Best accuracy L2 dimensionality L3 dimensionality  Seed  \\\n",
       "0      95.924765               800               800  1031   \n",
       "1      95.924765               800               800  1031   \n",
       "2      95.924765               800               800  1031   \n",
       "3      95.924765               800               800  1031   \n",
       "4      95.924765               800               800  1031   \n",
       "\n",
       "                                                  ID  \n",
       "0  Dense_Baselines cnn_out_channels=(32 32)linear...  \n",
       "1  Dense_Baselines cnn_out_channels=(32 32)linear...  \n",
       "2  Dense_Baselines cnn_out_channels=(32 32)linear...  \n",
       "3  Dense_Baselines cnn_out_channels=(32 32)linear...  \n",
       "4  Dense_Baselines cnn_out_channels=(32 32)linear...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dense_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>L1 Wt sparsity</th>\n",
       "      <th>L2 Wt sparsity</th>\n",
       "      <th>L3 Wt sparsity</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Best accuracy</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>163200.0</td>\n",
       "      <td>163200.000000</td>\n",
       "      <td>163200.000000</td>\n",
       "      <td>163200.000000</td>\n",
       "      <td>163200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.942712</td>\n",
       "      <td>0.978618</td>\n",
       "      <td>83.643030</td>\n",
       "      <td>96.517949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.061431</td>\n",
       "      <td>0.015289</td>\n",
       "      <td>21.322678</td>\n",
       "      <td>0.594588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>6.269592</td>\n",
       "      <td>94.004702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>83.307210</td>\n",
       "      <td>96.159875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>93.887147</td>\n",
       "      <td>96.630094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>95.728840</td>\n",
       "      <td>96.943574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.997000</td>\n",
       "      <td>0.995000</td>\n",
       "      <td>97.923197</td>\n",
       "      <td>97.923197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       L1 Wt sparsity  L2 Wt sparsity  L3 Wt sparsity       Accuracy  \\\n",
       "count        163200.0   163200.000000   163200.000000  163200.000000   \n",
       "mean              0.0        0.942712        0.978618      83.643030   \n",
       "std               0.0        0.061431        0.015289      21.322678   \n",
       "min               0.0        0.800000        0.950000       6.269592   \n",
       "25%               0.0        0.900000        0.975000      83.307210   \n",
       "50%               0.0        0.980000        0.980000      93.887147   \n",
       "75%               0.0        0.990000        0.990000      95.728840   \n",
       "max               0.0        0.997000        0.995000      97.923197   \n",
       "\n",
       "       Best accuracy  \n",
       "count  163200.000000  \n",
       "mean       96.517949  \n",
       "std         0.594588  \n",
       "min        94.004702  \n",
       "25%        96.159875  \n",
       "50%        96.630094  \n",
       "75%        96.943574  \n",
       "max        97.923197  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_all.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>L1 Wt sparsity</th>\n",
       "      <th>L2 Wt sparsity</th>\n",
       "      <th>L3 Wt sparsity</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Best accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86.035697</td>\n",
       "      <td>96.734326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.437721</td>\n",
       "      <td>0.329405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.150470</td>\n",
       "      <td>95.768025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.430643</td>\n",
       "      <td>96.541928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>95.532915</td>\n",
       "      <td>96.786834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.394984</td>\n",
       "      <td>96.943574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>97.413793</td>\n",
       "      <td>97.413793</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       L1 Wt sparsity  L2 Wt sparsity  L3 Wt sparsity     Accuracy  \\\n",
       "count          3000.0          3000.0          3000.0  3000.000000   \n",
       "mean              0.0             0.0             0.0    86.035697   \n",
       "std               0.0             0.0             0.0    20.437721   \n",
       "min               0.0             0.0             0.0     8.150470   \n",
       "25%               0.0             0.0             0.0    88.430643   \n",
       "50%               0.0             0.0             0.0    95.532915   \n",
       "75%               0.0             0.0             0.0    96.394984   \n",
       "max               0.0             0.0             0.0    97.413793   \n",
       "\n",
       "       Best accuracy  \n",
       "count    3000.000000  \n",
       "mean       96.734326  \n",
       "std         0.329405  \n",
       "min        95.768025  \n",
       "25%        96.541928  \n",
       "50%        96.786834  \n",
       "75%        96.943574  \n",
       "max        97.413793  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dense_all.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>L1 Wt sparsity</th>\n",
       "      <th>L2 Wt sparsity</th>\n",
       "      <th>L3 Wt sparsity</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Best accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50400.0</td>\n",
       "      <td>50400.000000</td>\n",
       "      <td>50400.000000</td>\n",
       "      <td>50400.000000</td>\n",
       "      <td>50400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.940686</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>95.030941</td>\n",
       "      <td>96.519257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.065013</td>\n",
       "      <td>0.014268</td>\n",
       "      <td>1.833086</td>\n",
       "      <td>0.630095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>68.495298</td>\n",
       "      <td>94.004702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>94.435737</td>\n",
       "      <td>96.159875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>95.532915</td>\n",
       "      <td>96.630094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>96.238245</td>\n",
       "      <td>96.982759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.997000</td>\n",
       "      <td>0.995000</td>\n",
       "      <td>97.923197</td>\n",
       "      <td>97.923197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       L1 Wt sparsity  L2 Wt sparsity  L3 Wt sparsity      Accuracy  \\\n",
       "count         50400.0    50400.000000    50400.000000  50400.000000   \n",
       "mean              0.0        0.940686        0.980000     95.030941   \n",
       "std               0.0        0.065013        0.014268      1.833086   \n",
       "min               0.0        0.800000        0.950000     68.495298   \n",
       "25%               0.0        0.900000        0.975000     94.435737   \n",
       "50%               0.0        0.980000        0.980000     95.532915   \n",
       "75%               0.0        0.990000        0.990000     96.238245   \n",
       "max               0.0        0.997000        0.995000     97.923197   \n",
       "\n",
       "       Best accuracy  \n",
       "count   50400.000000  \n",
       "mean       96.519257  \n",
       "std         0.630095  \n",
       "min        94.004702  \n",
       "25%        96.159875  \n",
       "50%        96.630094  \n",
       "75%        96.982759  \n",
       "max        97.923197  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_activations.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>L1 Wt sparsity</th>\n",
       "      <th>L2 Wt sparsity</th>\n",
       "      <th>L3 Wt sparsity</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Best accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>112800.0</td>\n",
       "      <td>112800.000000</td>\n",
       "      <td>112800.000000</td>\n",
       "      <td>112800.000000</td>\n",
       "      <td>112800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.943617</td>\n",
       "      <td>0.978000</td>\n",
       "      <td>78.554815</td>\n",
       "      <td>96.517364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.059740</td>\n",
       "      <td>0.015684</td>\n",
       "      <td>23.926276</td>\n",
       "      <td>0.578020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>6.269592</td>\n",
       "      <td>94.004702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>70.101881</td>\n",
       "      <td>96.159875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>90.713166</td>\n",
       "      <td>96.590909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>95.101881</td>\n",
       "      <td>96.943574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.995000</td>\n",
       "      <td>0.995000</td>\n",
       "      <td>97.766458</td>\n",
       "      <td>97.766458</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       L1 Wt sparsity  L2 Wt sparsity  L3 Wt sparsity       Accuracy  \\\n",
       "count        112800.0   112800.000000   112800.000000  112800.000000   \n",
       "mean              0.0        0.943617        0.978000      78.554815   \n",
       "std               0.0        0.059740        0.015684      23.926276   \n",
       "min               0.0        0.800000        0.950000       6.269592   \n",
       "25%               0.0        0.900000        0.975000      70.101881   \n",
       "50%               0.0        0.980000        0.980000      90.713166   \n",
       "75%               0.0        0.990000        0.990000      95.101881   \n",
       "max               0.0        0.995000        0.995000      97.766458   \n",
       "\n",
       "       Best accuracy  \n",
       "count  112800.000000  \n",
       "mean       96.517364  \n",
       "std         0.578020  \n",
       "min        94.004702  \n",
       "25%        96.159875  \n",
       "50%        96.590909  \n",
       "75%        96.943574  \n",
       "max        97.766458  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_wts.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataframe containing one row per trial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The dataframes defined here:**\n",
    "\n",
    "- dense_trial - dense experiments, where each row is one complete trial.\n",
    "- sparse_wts_trial - sparse weights experiments, where each row is one complete trial.\n",
    "- sparse_activations_trial - experiments with sparse weights and activations, where each row is one complete trial.\n",
    "\n",
    "In these dataframes, in each row, max_accuracy is the maximum test accuracy reached during the trial.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_trial_df(df):\n",
    "    dft= df.groupby([\"ID\",\"Seed\"]).agg(\n",
    "        # Max accuracy for that trial\n",
    "        max_accuracy=('Accuracy', \"max\"),\n",
    "        l1_channels=('L1 channels', \"first\"),\n",
    "        l2_channels=('L2 channels', \"first\"),\n",
    "        l3_n=(\"L3 N\", \"first\"),\n",
    "        l2_dim=('L2 dimensionality', \"first\"),\n",
    "        l3_dim=('L3 dimensionality', \"first\"),\n",
    "        l1_wt_sparsity=(\"L1 Wt sparsity\", \"first\"),\n",
    "        l2_wt_sparsity=(\"L2 Wt sparsity\", \"first\"),\n",
    "        l3_wt_sparsity=(\"L3 Wt sparsity\", \"first\"),\n",
    "        non_zero_params=('Non-zero params', \"first\"),\n",
    "        config=('ID', \"first\"),\n",
    "    )\n",
    "    dft[\"l2_wts_per_kernel\"] = dft.apply(lambda x: (x[\"l1_channels\"] * 25 * (1-x[\"l2_wt_sparsity\"])), axis=1)\n",
    "    dft[\"l3_wts_per_unit\"] = dft.apply(lambda x: (x[\"l2_channels\"] * 25 * (1-x[\"l3_wt_sparsity\"])), axis=1)\n",
    "    dft[\"dimensions\"] = dft.apply(lambda x: np.sqrt(x[\"l3_n\"] * x[\"l3_dim\"]), axis=1)\n",
    "    return dft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_wts_trial = create_trial_df(sparse_wts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_activations_trial = create_trial_df(sparse_activations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "dense_trial = create_trial_df(dense_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>max_accuracy</th>\n",
       "      <th>l1_channels</th>\n",
       "      <th>l2_channels</th>\n",
       "      <th>l3_n</th>\n",
       "      <th>l2_dim</th>\n",
       "      <th>l3_dim</th>\n",
       "      <th>l1_wt_sparsity</th>\n",
       "      <th>l2_wt_sparsity</th>\n",
       "      <th>l3_wt_sparsity</th>\n",
       "      <th>non_zero_params</th>\n",
       "      <th>config</th>\n",
       "      <th>l2_wts_per_kernel</th>\n",
       "      <th>l3_wts_per_unit</th>\n",
       "      <th>dimensions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th>Seed</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Sparse_Baselines cnn_out_channels=(64 128)cnn_weight_sparsity=(0.0 0.01)linear_n=(1000)weight_sparsity=(0.005)</th>\n",
       "      <th>1766</th>\n",
       "      <td>95.415361</td>\n",
       "      <td>64</td>\n",
       "      <td>128</td>\n",
       "      <td>1000</td>\n",
       "      <td>1600</td>\n",
       "      <td>3200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.995</td>\n",
       "      <td>32852</td>\n",
       "      <td>Sparse_Baselines cnn_out_channels=(64 128)cnn_...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1788.854382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2491</th>\n",
       "      <td>95.297806</td>\n",
       "      <td>64</td>\n",
       "      <td>128</td>\n",
       "      <td>1000</td>\n",
       "      <td>1600</td>\n",
       "      <td>3200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.995</td>\n",
       "      <td>32852</td>\n",
       "      <td>Sparse_Baselines cnn_out_channels=(64 128)cnn_...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1788.854382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4711</th>\n",
       "      <td>95.101881</td>\n",
       "      <td>64</td>\n",
       "      <td>128</td>\n",
       "      <td>1000</td>\n",
       "      <td>1600</td>\n",
       "      <td>3200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.995</td>\n",
       "      <td>32852</td>\n",
       "      <td>Sparse_Baselines cnn_out_channels=(64 128)cnn_...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1788.854382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5882</th>\n",
       "      <td>95.101881</td>\n",
       "      <td>64</td>\n",
       "      <td>128</td>\n",
       "      <td>1000</td>\n",
       "      <td>1600</td>\n",
       "      <td>3200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.995</td>\n",
       "      <td>32852</td>\n",
       "      <td>Sparse_Baselines cnn_out_channels=(64 128)cnn_...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1788.854382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sparse_Baselines cnn_out_channels=(64 128)cnn_weight_sparsity=(0.0 0.01)linear_n=(1000)weight_sparsity=(0.01)</th>\n",
       "      <th>2405</th>\n",
       "      <td>95.807210</td>\n",
       "      <td>64</td>\n",
       "      <td>128</td>\n",
       "      <td>1000</td>\n",
       "      <td>1600</td>\n",
       "      <td>3200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.990</td>\n",
       "      <td>48852</td>\n",
       "      <td>Sparse_Baselines cnn_out_channels=(64 128)cnn_...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1788.854382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         max_accuracy  \\\n",
       "ID                                                 Seed                 \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w... 1766     95.415361   \n",
       "                                                   2491     95.297806   \n",
       "                                                   4711     95.101881   \n",
       "                                                   5882     95.101881   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w... 2405     95.807210   \n",
       "\n",
       "                                                         l1_channels  \\\n",
       "ID                                                 Seed                \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w... 1766           64   \n",
       "                                                   2491           64   \n",
       "                                                   4711           64   \n",
       "                                                   5882           64   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w... 2405           64   \n",
       "\n",
       "                                                         l2_channels  l3_n  \\\n",
       "ID                                                 Seed                      \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w... 1766          128  1000   \n",
       "                                                   2491          128  1000   \n",
       "                                                   4711          128  1000   \n",
       "                                                   5882          128  1000   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w... 2405          128  1000   \n",
       "\n",
       "                                                         l2_dim  l3_dim  \\\n",
       "ID                                                 Seed                   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w... 1766    1600    3200   \n",
       "                                                   2491    1600    3200   \n",
       "                                                   4711    1600    3200   \n",
       "                                                   5882    1600    3200   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w... 2405    1600    3200   \n",
       "\n",
       "                                                         l1_wt_sparsity  \\\n",
       "ID                                                 Seed                   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w... 1766             0.0   \n",
       "                                                   2491             0.0   \n",
       "                                                   4711             0.0   \n",
       "                                                   5882             0.0   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w... 2405             0.0   \n",
       "\n",
       "                                                         l2_wt_sparsity  \\\n",
       "ID                                                 Seed                   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w... 1766            0.99   \n",
       "                                                   2491            0.99   \n",
       "                                                   4711            0.99   \n",
       "                                                   5882            0.99   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w... 2405            0.99   \n",
       "\n",
       "                                                         l3_wt_sparsity  \\\n",
       "ID                                                 Seed                   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w... 1766           0.995   \n",
       "                                                   2491           0.995   \n",
       "                                                   4711           0.995   \n",
       "                                                   5882           0.995   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w... 2405           0.990   \n",
       "\n",
       "                                                         non_zero_params  \\\n",
       "ID                                                 Seed                    \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w... 1766            32852   \n",
       "                                                   2491            32852   \n",
       "                                                   4711            32852   \n",
       "                                                   5882            32852   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w... 2405            48852   \n",
       "\n",
       "                                                                                                    config  \\\n",
       "ID                                                 Seed                                                      \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w... 1766  Sparse_Baselines cnn_out_channels=(64 128)cnn_...   \n",
       "                                                   2491  Sparse_Baselines cnn_out_channels=(64 128)cnn_...   \n",
       "                                                   4711  Sparse_Baselines cnn_out_channels=(64 128)cnn_...   \n",
       "                                                   5882  Sparse_Baselines cnn_out_channels=(64 128)cnn_...   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w... 2405  Sparse_Baselines cnn_out_channels=(64 128)cnn_...   \n",
       "\n",
       "                                                         l2_wts_per_kernel  \\\n",
       "ID                                                 Seed                      \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w... 1766               16.0   \n",
       "                                                   2491               16.0   \n",
       "                                                   4711               16.0   \n",
       "                                                   5882               16.0   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w... 2405               16.0   \n",
       "\n",
       "                                                         l3_wts_per_unit  \\\n",
       "ID                                                 Seed                    \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w... 1766             16.0   \n",
       "                                                   2491             16.0   \n",
       "                                                   4711             16.0   \n",
       "                                                   5882             16.0   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w... 2405             32.0   \n",
       "\n",
       "                                                          dimensions  \n",
       "ID                                                 Seed               \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w... 1766  1788.854382  \n",
       "                                                   2491  1788.854382  \n",
       "                                                   4711  1788.854382  \n",
       "                                                   5882  1788.854382  \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w... 2405  1788.854382  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_wts_trial.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_accuracy</th>\n",
       "      <th>l1_channels</th>\n",
       "      <th>l2_channels</th>\n",
       "      <th>l3_n</th>\n",
       "      <th>l2_dim</th>\n",
       "      <th>l3_dim</th>\n",
       "      <th>l1_wt_sparsity</th>\n",
       "      <th>l2_wt_sparsity</th>\n",
       "      <th>l3_wt_sparsity</th>\n",
       "      <th>non_zero_params</th>\n",
       "      <th>l2_wts_per_kernel</th>\n",
       "      <th>l3_wts_per_unit</th>\n",
       "      <th>dimensions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3760.000000</td>\n",
       "      <td>3760.000000</td>\n",
       "      <td>3760.000000</td>\n",
       "      <td>3760.000000</td>\n",
       "      <td>3760.000000</td>\n",
       "      <td>3760.000000</td>\n",
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       "      <td>3760.000000</td>\n",
       "      <td>3760.000000</td>\n",
       "      <td>3760.000000</td>\n",
       "      <td>3760.000000</td>\n",
       "      <td>3760.000000</td>\n",
       "      <td>3760.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>96.517364</td>\n",
       "      <td>74.212766</td>\n",
       "      <td>150.468085</td>\n",
       "      <td>1152.925532</td>\n",
       "      <td>1855.319149</td>\n",
       "      <td>3761.702128</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.943617</td>\n",
       "      <td>0.978000</td>\n",
       "      <td>124536.627660</td>\n",
       "      <td>99.659574</td>\n",
       "      <td>82.757447</td>\n",
       "      <td>1997.071238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.578094</td>\n",
       "      <td>14.918683</td>\n",
       "      <td>47.108169</td>\n",
       "      <td>474.353656</td>\n",
       "      <td>372.967086</td>\n",
       "      <td>1177.704232</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.059748</td>\n",
       "      <td>0.015686</td>\n",
       "      <td>91842.151866</td>\n",
       "      <td>100.028041</td>\n",
       "      <td>67.040479</td>\n",
       "      <td>496.868706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>94.004702</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>18514.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1095.445115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>96.159875</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>750.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>3200.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>60488.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>1549.193338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>96.590909</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>96856.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>1897.366596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>96.943574</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>4800.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>160267.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>2366.431913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>97.766458</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>5600.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.995000</td>\n",
       "      <td>0.995000</td>\n",
       "      <td>569308.000000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>3098.386677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       max_accuracy  l1_channels  l2_channels         l3_n       l2_dim  \\\n",
       "count   3760.000000  3760.000000  3760.000000  3760.000000  3760.000000   \n",
       "mean      96.517364    74.212766   150.468085  1152.925532  1855.319149   \n",
       "std        0.578094    14.918683    47.108169   474.353656   372.967086   \n",
       "min       94.004702    64.000000    64.000000   500.000000  1600.000000   \n",
       "25%       96.159875    64.000000   128.000000   750.000000  1600.000000   \n",
       "50%       96.590909    64.000000   160.000000  1000.000000  1600.000000   \n",
       "75%       96.943574    96.000000   192.000000  1500.000000  2400.000000   \n",
       "max       97.766458    96.000000   224.000000  2000.000000  2400.000000   \n",
       "\n",
       "            l3_dim  l1_wt_sparsity  l2_wt_sparsity  l3_wt_sparsity  \\\n",
       "count  3760.000000          3760.0     3760.000000     3760.000000   \n",
       "mean   3761.702128             0.0        0.943617        0.978000   \n",
       "std    1177.704232             0.0        0.059748        0.015686   \n",
       "min    1600.000000             0.0        0.800000        0.950000   \n",
       "25%    3200.000000             0.0        0.900000        0.975000   \n",
       "50%    4000.000000             0.0        0.980000        0.980000   \n",
       "75%    4800.000000             0.0        0.990000        0.990000   \n",
       "max    5600.000000             0.0        0.995000        0.995000   \n",
       "\n",
       "       non_zero_params  l2_wts_per_kernel  l3_wts_per_unit   dimensions  \n",
       "count      3760.000000        3760.000000      3760.000000  3760.000000  \n",
       "mean     124536.627660          99.659574        82.757447  1997.071238  \n",
       "std       91842.151866         100.028041        67.040479   496.868706  \n",
       "min       18514.000000           8.000000         8.000000  1095.445115  \n",
       "25%       60488.000000          16.000000        32.000000  1549.193338  \n",
       "50%       96856.000000          48.000000        64.000000  1897.366596  \n",
       "75%      160267.000000         160.000000       112.000000  2366.431913  \n",
       "max      569308.000000         320.000000       280.000000  3098.386677  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_wts_trial.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_accuracy</th>\n",
       "      <th>l1_channels</th>\n",
       "      <th>l2_channels</th>\n",
       "      <th>l3_n</th>\n",
       "      <th>l2_dim</th>\n",
       "      <th>l3_dim</th>\n",
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       "      <th>l3_wt_sparsity</th>\n",
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       "      <th>l2_wts_per_kernel</th>\n",
       "      <th>l3_wts_per_unit</th>\n",
       "      <th>dimensions</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1680.000000</td>\n",
       "      <td>1680.000000</td>\n",
       "      <td>1680.000000</td>\n",
       "      <td>1680.000000</td>\n",
       "      <td>1680.000000</td>\n",
       "      <td>1680.000000</td>\n",
       "      <td>1680.0</td>\n",
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       "      <td>1680.000000</td>\n",
       "      <td>1680.000000</td>\n",
       "      <td>1680.000000</td>\n",
       "      <td>1680.000000</td>\n",
       "      <td>1680.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>96.519257</td>\n",
       "      <td>68.571429</td>\n",
       "      <td>118.857143</td>\n",
       "      <td>1366.071429</td>\n",
       "      <td>1714.285714</td>\n",
       "      <td>2971.428571</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.940686</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>107470.700000</td>\n",
       "      <td>96.914286</td>\n",
       "      <td>56.571429</td>\n",
       "      <td>1954.344418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.630277</td>\n",
       "      <td>11.201002</td>\n",
       "      <td>49.532692</td>\n",
       "      <td>468.556304</td>\n",
       "      <td>280.025038</td>\n",
       "      <td>1238.317311</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.065032</td>\n",
       "      <td>0.014272</td>\n",
       "      <td>69886.093785</td>\n",
       "      <td>103.528176</td>\n",
       "      <td>41.655522</td>\n",
       "      <td>562.530480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>94.004702</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>750.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>18514.000000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1095.445115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>96.159875</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>56018.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>1549.193338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>96.630094</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>3200.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>87363.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>1788.854382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>96.982759</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>3200.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>142311.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>2234.775651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>97.923197</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>5600.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.997000</td>\n",
       "      <td>0.995000</td>\n",
       "      <td>388764.000000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>3346.640106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       max_accuracy  l1_channels  l2_channels         l3_n       l2_dim  \\\n",
       "count   1680.000000  1680.000000  1680.000000  1680.000000  1680.000000   \n",
       "mean      96.519257    68.571429   118.857143  1366.071429  1714.285714   \n",
       "std        0.630277    11.201002    49.532692   468.556304   280.025038   \n",
       "min       94.004702    64.000000    64.000000   750.000000  1600.000000   \n",
       "25%       96.159875    64.000000    96.000000  1000.000000  1600.000000   \n",
       "50%       96.630094    64.000000   128.000000  1500.000000  1600.000000   \n",
       "75%       96.982759    64.000000   128.000000  2000.000000  1600.000000   \n",
       "max       97.923197    96.000000   224.000000  2000.000000  2400.000000   \n",
       "\n",
       "            l3_dim  l1_wt_sparsity  l2_wt_sparsity  l3_wt_sparsity  \\\n",
       "count  1680.000000          1680.0     1680.000000     1680.000000   \n",
       "mean   2971.428571             0.0        0.940686        0.980000   \n",
       "std    1238.317311             0.0        0.065032        0.014272   \n",
       "min    1600.000000             0.0        0.800000        0.950000   \n",
       "25%    2400.000000             0.0        0.900000        0.975000   \n",
       "50%    3200.000000             0.0        0.980000        0.980000   \n",
       "75%    3200.000000             0.0        0.990000        0.990000   \n",
       "max    5600.000000             0.0        0.997000        0.995000   \n",
       "\n",
       "       non_zero_params  l2_wts_per_kernel  l3_wts_per_unit   dimensions  \n",
       "count      1680.000000        1680.000000      1680.000000  1680.000000  \n",
       "mean     107470.700000          96.914286        56.571429  1954.344418  \n",
       "std       69886.093785         103.528176        41.655522   562.530480  \n",
       "min       18514.000000           4.800000         8.000000  1095.445115  \n",
       "25%       56018.000000          16.000000        24.000000  1549.193338  \n",
       "50%       87363.000000          48.000000        48.000000  1788.854382  \n",
       "75%      142311.000000         160.000000        80.000000  2234.775651  \n",
       "max      388764.000000         320.000000       160.000000  3346.640106  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_activations_trial.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_accuracy</th>\n",
       "      <th>l1_channels</th>\n",
       "      <th>l2_channels</th>\n",
       "      <th>l3_n</th>\n",
       "      <th>l2_dim</th>\n",
       "      <th>l3_dim</th>\n",
       "      <th>l1_wt_sparsity</th>\n",
       "      <th>l2_wt_sparsity</th>\n",
       "      <th>l3_wt_sparsity</th>\n",
       "      <th>non_zero_params</th>\n",
       "      <th>l2_wts_per_kernel</th>\n",
       "      <th>l3_wts_per_unit</th>\n",
       "      <th>dimensions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>96.734326</td>\n",
       "      <td>57.600000</td>\n",
       "      <td>70.400000</td>\n",
       "      <td>1150.000000</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>1760.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.148050e+06</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>1760.000000</td>\n",
       "      <td>1332.588378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.331010</td>\n",
       "      <td>12.864484</td>\n",
       "      <td>37.506094</td>\n",
       "      <td>541.229427</td>\n",
       "      <td>321.612101</td>\n",
       "      <td>937.652345</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.576132e+06</td>\n",
       "      <td>321.612101</td>\n",
       "      <td>937.652345</td>\n",
       "      <td>500.714858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>95.768025</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.329760e+05</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>632.455532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>96.541928</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>750.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.106400e+05</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>894.427191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>96.786834</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.678908e+06</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>1264.911064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>96.943574</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.616354e+06</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>1549.193338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>97.413793</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>3200.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.632604e+06</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>3200.000000</td>\n",
       "      <td>2529.822128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       max_accuracy  l1_channels  l2_channels         l3_n       l2_dim  \\\n",
       "count    100.000000   100.000000   100.000000   100.000000   100.000000   \n",
       "mean      96.734326    57.600000    70.400000  1150.000000  1440.000000   \n",
       "std        0.331010    12.864484    37.506094   541.229427   321.612101   \n",
       "min       95.768025    32.000000    32.000000   500.000000   800.000000   \n",
       "25%       96.541928    64.000000    32.000000   750.000000  1600.000000   \n",
       "50%       96.786834    64.000000    64.000000  1000.000000  1600.000000   \n",
       "75%       96.943574    64.000000    96.000000  1500.000000  1600.000000   \n",
       "max       97.413793    64.000000   128.000000  2000.000000  1600.000000   \n",
       "\n",
       "            l3_dim  l1_wt_sparsity  l2_wt_sparsity  l3_wt_sparsity  \\\n",
       "count   100.000000           100.0           100.0           100.0   \n",
       "mean   1760.000000             0.0             0.0             0.0   \n",
       "std     937.652345             0.0             0.0             0.0   \n",
       "min     800.000000             0.0             0.0             0.0   \n",
       "25%     800.000000             0.0             0.0             0.0   \n",
       "50%    1600.000000             0.0             0.0             0.0   \n",
       "75%    2400.000000             0.0             0.0             0.0   \n",
       "max    3200.000000             0.0             0.0             0.0   \n",
       "\n",
       "       non_zero_params  l2_wts_per_kernel  l3_wts_per_unit   dimensions  \n",
       "count     1.000000e+02         100.000000       100.000000   100.000000  \n",
       "mean      2.148050e+06        1440.000000      1760.000000  1332.588378  \n",
       "std       1.576132e+06         321.612101       937.652345   500.714858  \n",
       "min       4.329760e+05         800.000000       800.000000   632.455532  \n",
       "25%       9.106400e+05        1600.000000       800.000000   894.427191  \n",
       "50%       1.678908e+06        1600.000000      1600.000000  1264.911064  \n",
       "75%       2.616354e+06        1600.000000      2400.000000  1549.193338  \n",
       "max       6.632604e+06        1600.000000      3200.000000  2529.822128  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dense_trial.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(dpi=125)\n",
    "plt.title(\"Accuracy vs non-zero parameters\")\n",
    "plt.xlabel(\"Non-zero parameters\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.ylim(93, 98)\n",
    "plt.scatter(sparse_wts_trial[\"non_zero_params\"], sparse_wts_trial[\"max_accuracy\"], label=\"Sparse networks\", s=2)\n",
    "plt.scatter(dense_trial[\"non_zero_params\"], dense_trial[\"max_accuracy\"], label=\"Dense networks\", s=2)\n",
    "plt.xticks(np.arange(0, 7000000, 2000000))\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig(\"plots/overall_accuracies.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(dpi=125)\n",
    "plt.title(\"Accuracy vs non-zero parameters (sparse)\")\n",
    "plt.xlabel(\"Non-zero parameters\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.ylim(93, 98)\n",
    "plt.xticks(np.arange(0, 400001, 100000))\n",
    "plt.scatter(sparse_wts_trial[\"non_zero_params\"], sparse_wts_trial[\"max_accuracy\"], label=\"Sparse networks\", s=2)\n",
    "plt.savefig(\"plots/overall_accuracies_sparse.png\", dpi=300)\n",
    "plt.hlines(97.4, 0, 600001, label=\"Best dense network\", linestyle=\"dashed\", colors=\"red\")\n",
    "plt.xticks(np.arange(0, 600001, 100000))\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig(\"plots/overall_accuracies_sparse2.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataframe with one row per network configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The dataframes defined here:**\n",
    "\n",
    "Each row contains the mean max_accuracy for each unique network configuration (ID), averaged over all the random seeds.\n",
    "\n",
    "- dense_id - dense experiments, where each row is one complete trial.\n",
    "- sparse_wts_id - sparse weights experiments, where each row is one complete trial.\n",
    "- sparse_activations_id - experiments with sparse weights and activations, where each row is one complete trial.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_id_dataframe(df):\n",
    "    dfi= df.groupby([\"config\"]).agg(\n",
    "        mean_accuracy=('max_accuracy', \"mean\"),\n",
    "        l1_channels=('l1_channels', \"first\"),\n",
    "        l2_channels=('l2_channels', \"first\"),\n",
    "        l3_n=(\"l3_n\", \"first\"),\n",
    "        l2_dim=('l2_dim', \"first\"),\n",
    "        l3_dim=('l3_dim', \"first\"),\n",
    "        l1_wt_sparsity=(\"l1_wt_sparsity\", \"first\"),\n",
    "        l2_wt_sparsity=(\"l2_wt_sparsity\", \"first\"),\n",
    "        l3_wt_sparsity=(\"l3_wt_sparsity\", \"first\"),\n",
    "        l2_wts_per_kernel=(\"l2_wts_per_kernel\", \"first\"),\n",
    "        l3_wts_per_unit=(\"l3_wts_per_unit\", \"first\"),\n",
    "        dimensions=('dimensions', \"first\"),\n",
    "        non_zero_params=('non_zero_params', \"first\"),\n",
    "        config=('config', \"first\"),\n",
    "        num_trials=('config', \"count\"),\n",
    "    )\n",
    "    return dfi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_wts_id = create_id_dataframe(sparse_wts_trial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "dense_id = create_id_dataframe(dense_trial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_activations_id = create_id_dataframe(sparse_activations_trial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_accuracy</th>\n",
       "      <th>l1_channels</th>\n",
       "      <th>l2_channels</th>\n",
       "      <th>l3_n</th>\n",
       "      <th>l2_dim</th>\n",
       "      <th>l3_dim</th>\n",
       "      <th>l1_wt_sparsity</th>\n",
       "      <th>l2_wt_sparsity</th>\n",
       "      <th>l3_wt_sparsity</th>\n",
       "      <th>l2_wts_per_kernel</th>\n",
       "      <th>l3_wts_per_unit</th>\n",
       "      <th>dimensions</th>\n",
       "      <th>non_zero_params</th>\n",
       "      <th>config</th>\n",
       "      <th>num_trials</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>config</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sparse_Baselines cnn_out_channels=(64 128)cnn_weight_sparsity=(0.0 0.01)linear_n=(1000)weight_sparsity=(0.005)</th>\n",
       "      <td>95.229232</td>\n",
       "      <td>64</td>\n",
       "      <td>128</td>\n",
       "      <td>1000</td>\n",
       "      <td>1600</td>\n",
       "      <td>3200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.995</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1788.854382</td>\n",
       "      <td>32852</td>\n",
       "      <td>Sparse_Baselines cnn_out_channels=(64 128)cnn_...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sparse_Baselines cnn_out_channels=(64 128)cnn_weight_sparsity=(0.0 0.01)linear_n=(1000)weight_sparsity=(0.01)</th>\n",
       "      <td>95.856191</td>\n",
       "      <td>64</td>\n",
       "      <td>128</td>\n",
       "      <td>1000</td>\n",
       "      <td>1600</td>\n",
       "      <td>3200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.990</td>\n",
       "      <td>16.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1788.854382</td>\n",
       "      <td>48852</td>\n",
       "      <td>Sparse_Baselines cnn_out_channels=(64 128)cnn_...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sparse_Baselines cnn_out_channels=(64 128)cnn_weight_sparsity=(0.0 0.01)linear_n=(1000)weight_sparsity=(0.02)</th>\n",
       "      <td>96.189263</td>\n",
       "      <td>64</td>\n",
       "      <td>128</td>\n",
       "      <td>1000</td>\n",
       "      <td>1600</td>\n",
       "      <td>3200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.980</td>\n",
       "      <td>16.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1788.854382</td>\n",
       "      <td>80852</td>\n",
       "      <td>Sparse_Baselines cnn_out_channels=(64 128)cnn_...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sparse_Baselines cnn_out_channels=(64 128)cnn_weight_sparsity=(0.0 0.01)linear_n=(1000)weight_sparsity=(0.025)</th>\n",
       "      <td>96.385188</td>\n",
       "      <td>64</td>\n",
       "      <td>128</td>\n",
       "      <td>1000</td>\n",
       "      <td>1600</td>\n",
       "      <td>3200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.975</td>\n",
       "      <td>16.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1788.854382</td>\n",
       "      <td>96852</td>\n",
       "      <td>Sparse_Baselines cnn_out_channels=(64 128)cnn_...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sparse_Baselines cnn_out_channels=(64 128)cnn_weight_sparsity=(0.0 0.01)linear_n=(1000)weight_sparsity=(0.05)</th>\n",
       "      <td>96.551724</td>\n",
       "      <td>64</td>\n",
       "      <td>128</td>\n",
       "      <td>1000</td>\n",
       "      <td>1600</td>\n",
       "      <td>3200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.950</td>\n",
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       "      <td>160.0</td>\n",
       "      <td>1788.854382</td>\n",
       "      <td>176852</td>\n",
       "      <td>Sparse_Baselines cnn_out_channels=(64 128)cnn_...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    mean_accuracy  \\\n",
       "config                                                              \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...      95.229232   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...      95.856191   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...      96.189263   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...      96.385188   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...      96.551724   \n",
       "\n",
       "                                                    l1_channels  l2_channels  \\\n",
       "config                                                                         \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...           64          128   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...           64          128   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...           64          128   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...           64          128   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...           64          128   \n",
       "\n",
       "                                                    l3_n  l2_dim  l3_dim  \\\n",
       "config                                                                     \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...  1000    1600    3200   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...  1000    1600    3200   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...  1000    1600    3200   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...  1000    1600    3200   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...  1000    1600    3200   \n",
       "\n",
       "                                                    l1_wt_sparsity  \\\n",
       "config                                                               \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...             0.0   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...             0.0   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...             0.0   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...             0.0   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...             0.0   \n",
       "\n",
       "                                                    l2_wt_sparsity  \\\n",
       "config                                                               \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...            0.99   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...            0.99   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...            0.99   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...            0.99   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...            0.99   \n",
       "\n",
       "                                                    l3_wt_sparsity  \\\n",
       "config                                                               \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...           0.995   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...           0.990   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...           0.980   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...           0.975   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...           0.950   \n",
       "\n",
       "                                                    l2_wts_per_kernel  \\\n",
       "config                                                                  \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...               16.0   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...               16.0   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...               16.0   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...               16.0   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...               16.0   \n",
       "\n",
       "                                                    l3_wts_per_unit  \\\n",
       "config                                                                \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...             16.0   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...             32.0   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...             64.0   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...             80.0   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...            160.0   \n",
       "\n",
       "                                                     dimensions  \\\n",
       "config                                                            \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...  1788.854382   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...  1788.854382   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...  1788.854382   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...  1788.854382   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...  1788.854382   \n",
       "\n",
       "                                                    non_zero_params  \\\n",
       "config                                                                \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...            32852   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...            48852   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...            80852   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...            96852   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...           176852   \n",
       "\n",
       "                                                                                               config  \\\n",
       "config                                                                                                  \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...  Sparse_Baselines cnn_out_channels=(64 128)cnn_...   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...  Sparse_Baselines cnn_out_channels=(64 128)cnn_...   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...  Sparse_Baselines cnn_out_channels=(64 128)cnn_...   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...  Sparse_Baselines cnn_out_channels=(64 128)cnn_...   \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...  Sparse_Baselines cnn_out_channels=(64 128)cnn_...   \n",
       "\n",
       "                                                    num_trials  \n",
       "config                                                          \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...           4  \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...           4  \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...           4  \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...           4  \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...           4  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_wts_id.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_accuracy</th>\n",
       "      <th>l1_channels</th>\n",
       "      <th>l2_channels</th>\n",
       "      <th>l3_n</th>\n",
       "      <th>l2_dim</th>\n",
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       "      <th>num_trials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>940.000000</td>\n",
       "      <td>940.000000</td>\n",
       "      <td>940.000000</td>\n",
       "      <td>940.000000</td>\n",
       "      <td>940.000000</td>\n",
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       "      <td>940.000000</td>\n",
       "      <td>940.000000</td>\n",
       "      <td>940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>96.517364</td>\n",
       "      <td>74.212766</td>\n",
       "      <td>150.468085</td>\n",
       "      <td>1152.925532</td>\n",
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       "      <td>99.659574</td>\n",
       "      <td>82.757447</td>\n",
       "      <td>1997.071238</td>\n",
       "      <td>124536.627660</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.541913</td>\n",
       "      <td>14.924640</td>\n",
       "      <td>47.126979</td>\n",
       "      <td>474.543057</td>\n",
       "      <td>373.116005</td>\n",
       "      <td>1178.174468</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.059772</td>\n",
       "      <td>0.015693</td>\n",
       "      <td>100.067980</td>\n",
       "      <td>67.067247</td>\n",
       "      <td>497.067096</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>94.298589</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1095.445115</td>\n",
       "      <td>18514.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>96.196611</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>750.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>3200.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>1549.193338</td>\n",
       "      <td>60488.000000</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>96.590909</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>1897.366596</td>\n",
       "      <td>96856.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>96.933777</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>4800.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>2366.431913</td>\n",
       "      <td>160267.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>97.531348</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2400.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.995000</td>\n",
       "      <td>0.995000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>3098.386677</td>\n",
       "      <td>569308.000000</td>\n",
       "      <td>4.0</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "       mean_accuracy  l1_channels  l2_channels         l3_n       l2_dim  \\\n",
       "count     940.000000   940.000000   940.000000   940.000000   940.000000   \n",
       "mean       96.517364    74.212766   150.468085  1152.925532  1855.319149   \n",
       "std         0.541913    14.924640    47.126979   474.543057   373.116005   \n",
       "min        94.298589    64.000000    64.000000   500.000000  1600.000000   \n",
       "25%        96.196611    64.000000   128.000000   750.000000  1600.000000   \n",
       "50%        96.590909    64.000000   160.000000  1000.000000  1600.000000   \n",
       "75%        96.933777    96.000000   192.000000  1500.000000  2400.000000   \n",
       "max        97.531348    96.000000   224.000000  2000.000000  2400.000000   \n",
       "\n",
       "            l3_dim  l1_wt_sparsity  l2_wt_sparsity  l3_wt_sparsity  \\\n",
       "count   940.000000           940.0      940.000000      940.000000   \n",
       "mean   3761.702128             0.0        0.943617        0.978000   \n",
       "std    1178.174468             0.0        0.059772        0.015693   \n",
       "min    1600.000000             0.0        0.800000        0.950000   \n",
       "25%    3200.000000             0.0        0.900000        0.975000   \n",
       "50%    4000.000000             0.0        0.980000        0.980000   \n",
       "75%    4800.000000             0.0        0.990000        0.990000   \n",
       "max    5600.000000             0.0        0.995000        0.995000   \n",
       "\n",
       "       l2_wts_per_kernel  l3_wts_per_unit   dimensions  non_zero_params  \\\n",
       "count         940.000000       940.000000   940.000000       940.000000   \n",
       "mean           99.659574        82.757447  1997.071238    124536.627660   \n",
       "std           100.067980        67.067247   497.067096     91878.822721   \n",
       "min             8.000000         8.000000  1095.445115     18514.000000   \n",
       "25%            16.000000        32.000000  1549.193338     60488.000000   \n",
       "50%            48.000000        64.000000  1897.366596     96856.000000   \n",
       "75%           160.000000       112.000000  2366.431913    160267.000000   \n",
       "max           320.000000       280.000000  3098.386677    569308.000000   \n",
       "\n",
       "       num_trials  \n",
       "count       940.0  \n",
       "mean          4.0  \n",
       "std           0.0  \n",
       "min           4.0  \n",
       "25%           4.0  \n",
       "50%           4.0  \n",
       "75%           4.0  \n",
       "max           4.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_wts_id.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>num_trials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>420.000000</td>\n",
       "      <td>420.000000</td>\n",
       "      <td>420.000000</td>\n",
       "      <td>420.000000</td>\n",
       "      <td>420.000000</td>\n",
       "      <td>420.000000</td>\n",
       "      <td>420.0</td>\n",
       "      <td>420.000000</td>\n",
       "      <td>420.000000</td>\n",
       "      <td>420.000000</td>\n",
       "      <td>420.000000</td>\n",
       "      <td>420.000000</td>\n",
       "      <td>420.000000</td>\n",
       "      <td>420.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>96.519257</td>\n",
       "      <td>68.571429</td>\n",
       "      <td>118.857143</td>\n",
       "      <td>1366.071429</td>\n",
       "      <td>1714.285714</td>\n",
       "      <td>2971.428571</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.940686</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>96.914286</td>\n",
       "      <td>56.571429</td>\n",
       "      <td>1954.344418</td>\n",
       "      <td>107470.700000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.608600</td>\n",
       "      <td>11.211022</td>\n",
       "      <td>49.577004</td>\n",
       "      <td>468.975469</td>\n",
       "      <td>280.275545</td>\n",
       "      <td>1239.425095</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.065090</td>\n",
       "      <td>0.014285</td>\n",
       "      <td>103.620792</td>\n",
       "      <td>41.692786</td>\n",
       "      <td>563.033713</td>\n",
       "      <td>69948.613041</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>94.288793</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>750.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1095.445115</td>\n",
       "      <td>18514.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>96.159875</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>1549.193338</td>\n",
       "      <td>56018.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>96.634992</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>3200.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>1788.854382</td>\n",
       "      <td>87363.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>96.992555</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>3200.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>2234.775651</td>\n",
       "      <td>142311.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>97.521552</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>5600.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.997000</td>\n",
       "      <td>0.995000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>3346.640106</td>\n",
       "      <td>388764.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean_accuracy  l1_channels  l2_channels         l3_n       l2_dim  \\\n",
       "count     420.000000   420.000000   420.000000   420.000000   420.000000   \n",
       "mean       96.519257    68.571429   118.857143  1366.071429  1714.285714   \n",
       "std         0.608600    11.211022    49.577004   468.975469   280.275545   \n",
       "min        94.288793    64.000000    64.000000   750.000000  1600.000000   \n",
       "25%        96.159875    64.000000    96.000000  1000.000000  1600.000000   \n",
       "50%        96.634992    64.000000   128.000000  1500.000000  1600.000000   \n",
       "75%        96.992555    64.000000   128.000000  2000.000000  1600.000000   \n",
       "max        97.521552    96.000000   224.000000  2000.000000  2400.000000   \n",
       "\n",
       "            l3_dim  l1_wt_sparsity  l2_wt_sparsity  l3_wt_sparsity  \\\n",
       "count   420.000000           420.0      420.000000      420.000000   \n",
       "mean   2971.428571             0.0        0.940686        0.980000   \n",
       "std    1239.425095             0.0        0.065090        0.014285   \n",
       "min    1600.000000             0.0        0.800000        0.950000   \n",
       "25%    2400.000000             0.0        0.900000        0.975000   \n",
       "50%    3200.000000             0.0        0.980000        0.980000   \n",
       "75%    3200.000000             0.0        0.990000        0.990000   \n",
       "max    5600.000000             0.0        0.997000        0.995000   \n",
       "\n",
       "       l2_wts_per_kernel  l3_wts_per_unit   dimensions  non_zero_params  \\\n",
       "count         420.000000       420.000000   420.000000       420.000000   \n",
       "mean           96.914286        56.571429  1954.344418    107470.700000   \n",
       "std           103.620792        41.692786   563.033713     69948.613041   \n",
       "min             4.800000         8.000000  1095.445115     18514.000000   \n",
       "25%            16.000000        24.000000  1549.193338     56018.000000   \n",
       "50%            48.000000        48.000000  1788.854382     87363.000000   \n",
       "75%           160.000000        80.000000  2234.775651    142311.000000   \n",
       "max           320.000000       160.000000  3346.640106    388764.000000   \n",
       "\n",
       "       num_trials  \n",
       "count       420.0  \n",
       "mean          4.0  \n",
       "std           0.0  \n",
       "min           4.0  \n",
       "25%           4.0  \n",
       "50%           4.0  \n",
       "75%           4.0  \n",
       "max           4.0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_activations_id.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_accuracy</th>\n",
       "      <th>l1_channels</th>\n",
       "      <th>l2_channels</th>\n",
       "      <th>l3_n</th>\n",
       "      <th>l2_dim</th>\n",
       "      <th>l3_dim</th>\n",
       "      <th>l1_wt_sparsity</th>\n",
       "      <th>l2_wt_sparsity</th>\n",
       "      <th>l3_wt_sparsity</th>\n",
       "      <th>l2_wts_per_kernel</th>\n",
       "      <th>l3_wts_per_unit</th>\n",
       "      <th>dimensions</th>\n",
       "      <th>non_zero_params</th>\n",
       "      <th>num_trials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>2.500000e+01</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>96.734326</td>\n",
       "      <td>57.600000</td>\n",
       "      <td>70.400000</td>\n",
       "      <td>1150.000000</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>1760.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>1760.000000</td>\n",
       "      <td>1332.588378</td>\n",
       "      <td>2.148050e+06</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.239589</td>\n",
       "      <td>13.063945</td>\n",
       "      <td>38.087618</td>\n",
       "      <td>549.621082</td>\n",
       "      <td>326.598632</td>\n",
       "      <td>952.190457</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>326.598632</td>\n",
       "      <td>952.190457</td>\n",
       "      <td>508.478342</td>\n",
       "      <td>1.600570e+06</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>96.169671</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>632.455532</td>\n",
       "      <td>4.329760e+05</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>96.639890</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>750.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>894.427191</td>\n",
       "      <td>9.106400e+05</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>96.806426</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>1264.911064</td>\n",
       "      <td>1.678908e+06</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>96.904389</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>1549.193338</td>\n",
       "      <td>2.616354e+06</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>97.031740</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>3200.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>3200.000000</td>\n",
       "      <td>2529.822128</td>\n",
       "      <td>6.632604e+06</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean_accuracy  l1_channels  l2_channels         l3_n       l2_dim  \\\n",
       "count      25.000000    25.000000    25.000000    25.000000    25.000000   \n",
       "mean       96.734326    57.600000    70.400000  1150.000000  1440.000000   \n",
       "std         0.239589    13.063945    38.087618   549.621082   326.598632   \n",
       "min        96.169671    32.000000    32.000000   500.000000   800.000000   \n",
       "25%        96.639890    64.000000    32.000000   750.000000  1600.000000   \n",
       "50%        96.806426    64.000000    64.000000  1000.000000  1600.000000   \n",
       "75%        96.904389    64.000000    96.000000  1500.000000  1600.000000   \n",
       "max        97.031740    64.000000   128.000000  2000.000000  1600.000000   \n",
       "\n",
       "            l3_dim  l1_wt_sparsity  l2_wt_sparsity  l3_wt_sparsity  \\\n",
       "count    25.000000            25.0            25.0            25.0   \n",
       "mean   1760.000000             0.0             0.0             0.0   \n",
       "std     952.190457             0.0             0.0             0.0   \n",
       "min     800.000000             0.0             0.0             0.0   \n",
       "25%     800.000000             0.0             0.0             0.0   \n",
       "50%    1600.000000             0.0             0.0             0.0   \n",
       "75%    2400.000000             0.0             0.0             0.0   \n",
       "max    3200.000000             0.0             0.0             0.0   \n",
       "\n",
       "       l2_wts_per_kernel  l3_wts_per_unit   dimensions  non_zero_params  \\\n",
       "count          25.000000        25.000000    25.000000     2.500000e+01   \n",
       "mean         1440.000000      1760.000000  1332.588378     2.148050e+06   \n",
       "std           326.598632       952.190457   508.478342     1.600570e+06   \n",
       "min           800.000000       800.000000   632.455532     4.329760e+05   \n",
       "25%          1600.000000       800.000000   894.427191     9.106400e+05   \n",
       "50%          1600.000000      1600.000000  1264.911064     1.678908e+06   \n",
       "75%          1600.000000      2400.000000  1549.193338     2.616354e+06   \n",
       "max          1600.000000      3200.000000  2529.822128     6.632604e+06   \n",
       "\n",
       "       num_trials  \n",
       "count        25.0  \n",
       "mean          4.0  \n",
       "std           0.0  \n",
       "min           4.0  \n",
       "25%           4.0  \n",
       "50%           4.0  \n",
       "75%           4.0  \n",
       "max           4.0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dense_id.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_dense_accuracy = dense_id[\"mean_accuracy\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97.03173981191222"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_dense_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(dpi=125)\n",
    "plt.title(\"Accuracy vs non-zero parameters\")\n",
    "plt.xlabel(\"Non-zero parameters\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.ylim(93, 98)\n",
    "plt.scatter(sparse_wts_id[\"non_zero_params\"], sparse_wts_id[\"mean_accuracy\"], label=\"Sparse networks\", s=2)\n",
    "plt.scatter(dense_id[\"non_zero_params\"], dense_id[\"mean_accuracy\"], label=\"Dense networks\", s=10)\n",
    "plt.xticks(np.arange(0, 7000000, 2000000))\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig(\"plots/accuracies_all_configurations.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(dpi=125)\n",
    "plt.title(\"Accuracy vs non-zero parameters (sparse)\")\n",
    "plt.xlabel(\"Non-zero parameters\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.ylim(93, 98)\n",
    "plt.xticks(np.arange(0, 400001, 100000))\n",
    "plt.scatter(sparse_wts_id[\"non_zero_params\"], sparse_wts_id[\"mean_accuracy\"], label=\"Sparse networks\", s=2)\n",
    "plt.hlines(best_dense_accuracy, 0, 600001, label=\"Best dense network\", linestyle=\"dashed\", colors=\"red\")\n",
    "plt.xticks(np.arange(0, 600001, 100000))\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig(\"plots/accuracies_sparse_wts_configurations.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(dpi=125)\n",
    "plt.title(\"Accuracy vs non-zero parameters (sparse)\")\n",
    "plt.xlabel(\"Non-zero parameters\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.ylim(93, 98)\n",
    "plt.xticks(np.arange(0, 400001, 100000))\n",
    "plt.scatter(sparse_wts_id[\"non_zero_params\"], sparse_wts_id[\"mean_accuracy\"], label=\"Sparse weights only\", s=2)\n",
    "plt.xticks(np.arange(0, 600001, 100000))\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig(\"plots/accuracies_sparse_configurations.png\", dpi=300)\n",
    "plt.scatter(sparse_activations_id[\"non_zero_params\"], sparse_activations_id[\"mean_accuracy\"], label=\"Sparse weights + activations\", s=2, c=\"red\")\n",
    "# plt.hlines(best_dense_accuracy, 0, 600001, label=\"Best dense network\", linestyle=\"dashed\", colors=\"green\")\n",
    "plt.xticks(np.arange(0, 600001, 100000))\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig(\"plots/accuracies_all_sparse_configurations.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Each dot in the plot above shows one network configuration. The accuracy is an average over 4 random seeds.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting accuracy vs dimensionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f87890a2f10>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(dpi=125)\n",
    "plt.title(\"Accuracy vs dimensionality (sparse)\")\n",
    "plt.xlabel(\"Dimensionality\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.ylim(93, 98)\n",
    "plt.xticks(np.arange(0, 400001, 100000))\n",
    "plt.scatter(sparse_wts_id[\"dimensions\"], sparse_wts_id[\"mean_accuracy\"], label=\"Sparse weights only\", s=2)\n",
    "#plt.scatter(sparse_activations_id[\"dimensions\"], sparse_activations_id[\"mean_accuracy\"], label=\"Sparse weights + activations\", s=2, c=\"red\")\n",
    "plt.xticks(np.arange(0, 3100, 500))\n",
    "plt.legend(loc=\"lower right\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f87808eac50>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(dpi=125)\n",
    "plt.title(\"Accuracy vs L2 size (sparse)\")\n",
    "plt.xlabel(\"Dimensionality\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.ylim(93, 98)\n",
    "plt.scatter(sparse_wts_id[\"l3_dim\"], sparse_wts_id[\"mean_accuracy\"], label=\"Sparse weights only\", s=2)\n",
    "#plt.scatter(sparse_activations_id[\"dimensions\"], sparse_activations_id[\"mean_accuracy\"], label=\"Sparse weights + activations\", s=2, c=\"red\")\n",
    "plt.xticks(np.arange(0, 8000, 1000))\n",
    "plt.legend(loc=\"lower right\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_wts_dimensionality = sparse_wts_id.groupby(\"dimensions\").agg(\n",
    "        accuracy=('mean_accuracy', \"max\"),\n",
    "        dimensions=('dimensions', \"first\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_activations_dimensionality = sparse_activations_id.groupby(\"dimensions\").agg(\n",
    "        accuracy=('mean_accuracy', \"max\"),\n",
    "        dimensions=('dimensions', \"first\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(dpi=125)\n",
    "plt.title(\"Best accuracy vs dimensionality  (sparse)\")\n",
    "plt.xlabel(\"Dimensionality\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.ylim(96, 98)\n",
    "#plt.xticks(np.arange(0, 400001, 100000))\n",
    "plt.scatter(sparse_wts_dimensionality[\"dimensions\"], sparse_wts_dimensionality[\"accuracy\"], label=\"Sparse weights only\", s=10)\n",
    "#plt.hlines(best_dense_accuracy, 1000, 3000, label=\"Best dense network\", linestyle=\"dashed\", colors=\"red\")\n",
    "plt.xticks(np.arange(1000, 3100, 500))\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig(\"plots/accuracies_vs_dimensions_sparse.png\", dpi=300)\n",
    "plt.scatter(sparse_activations_dimensionality[\"dimensions\"], sparse_activations_dimensionality[\"accuracy\"], label=\"Sparse weights + activations\", s=10, c=\"red\")\n",
    "plt.xticks(np.arange(1000, 3100, 500))\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig(\"plots/accuracies_vs_dimensions_sparse_all.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Each dot in the plot above shows one network configuration. The accuracy is the max accuracy for that dimensionality (pareto frontier)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_wts_l3_dim = sparse_wts_id.groupby(\"l3_dim\").agg(\n",
    "        accuracy=('mean_accuracy', \"max\"),\n",
    "        l3_dim=('l3_dim', \"first\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_activations_l3_dim = sparse_activations_id.groupby(\"l3_dim\").agg(\n",
    "        accuracy=('mean_accuracy', \"max\"),\n",
    "        l3_dim=('l3_dim', \"first\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(dpi=125)\n",
    "plt.title(\"Best accuracy vs L3 weight dimensionality  (sparse)\")\n",
    "plt.xlabel(\"Dimensionality\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.ylim(96, 98)\n",
    "plt.scatter(sparse_wts_l3_dim[\"l3_dim\"], sparse_wts_l3_dim[\"accuracy\"], label=\"Sparse weights only\", s=10)\n",
    "#plt.scatter(sparse_activations_l3_dim[\"l3_dim\"], sparse_activations_l3_dim[\"accuracy\"], label=\"Sparse weights + activations\", s=10, c=\"red\")\n",
    "plt.xticks(np.arange(1000, 7100, 1000))\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig(\"plots/accuracies_vs_l3_dimensions.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_wts_l3_n = sparse_wts_id.groupby(\"l3_n\").agg(\n",
    "        accuracy=('mean_accuracy', \"max\"),\n",
    "        l3_n=('l3_n', \"first\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_activations_l3_n = sparse_activations_id.groupby(\"l3_n\").agg(\n",
    "        accuracy=('mean_accuracy', \"max\"),\n",
    "        l3_n=('l3_n', \"first\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(dpi=125)\n",
    "plt.title(\"Best accuracy vs L3 N  (sparse)\")\n",
    "plt.xlabel(\"Dimensionality\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.ylim(96, 98)\n",
    "plt.scatter(sparse_wts_l3_n[\"l3_n\"], sparse_wts_l3_n[\"accuracy\"], label=\"Sparse weights only\", s=10)\n",
    "#plt.scatter(sparse_activations_l3_n[\"l3_n\"], sparse_activations_l3_n[\"accuracy\"], label=\"Sparse weights + activations\", s=10, c=\"red\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig(\"plots/accuracies_vs_l3_n.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataframe containing accurate network configurations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_wts_accurate = sparse_wts_id[sparse_wts_id[\"mean_accuracy\"] >= best_dense_accuracy]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_accuracy</th>\n",
       "      <th>l1_channels</th>\n",
       "      <th>l2_channels</th>\n",
       "      <th>l3_n</th>\n",
       "      <th>l2_dim</th>\n",
       "      <th>l3_dim</th>\n",
       "      <th>l1_wt_sparsity</th>\n",
       "      <th>l2_wt_sparsity</th>\n",
       "      <th>l3_wt_sparsity</th>\n",
       "      <th>l2_wts_per_kernel</th>\n",
       "      <th>l3_wts_per_unit</th>\n",
       "      <th>dimensions</th>\n",
       "      <th>non_zero_params</th>\n",
       "      <th>num_trials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.0</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>97.171135</td>\n",
       "      <td>72.658824</td>\n",
       "      <td>161.505882</td>\n",
       "      <td>1382.352941</td>\n",
       "      <td>1816.470588</td>\n",
       "      <td>4037.647059</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.879941</td>\n",
       "      <td>0.967294</td>\n",
       "      <td>209.223529</td>\n",
       "      <td>130.894118</td>\n",
       "      <td>2286.818599</td>\n",
       "      <td>221405.929412</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.107645</td>\n",
       "      <td>14.258437</td>\n",
       "      <td>41.308659</td>\n",
       "      <td>470.408728</td>\n",
       "      <td>356.460927</td>\n",
       "      <td>1032.716470</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.059251</td>\n",
       "      <td>0.015737</td>\n",
       "      <td>90.258324</td>\n",
       "      <td>72.460072</td>\n",
       "      <td>451.023283</td>\n",
       "      <td>100271.520471</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>97.031740</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1341.640786</td>\n",
       "      <td>80220.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>97.090517</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>3200.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1897.366596</td>\n",
       "      <td>149512.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>97.149295</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>2190.890230</td>\n",
       "      <td>196850.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>97.217868</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>4800.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>2683.281573</td>\n",
       "      <td>269481.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>97.531348</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>5600.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.995000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>3098.386677</td>\n",
       "      <td>569308.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean_accuracy  l1_channels  l2_channels         l3_n       l2_dim  \\\n",
       "count     170.000000   170.000000   170.000000   170.000000   170.000000   \n",
       "mean       97.171135    72.658824   161.505882  1382.352941  1816.470588   \n",
       "std         0.107645    14.258437    41.308659   470.408728   356.460927   \n",
       "min        97.031740    64.000000    64.000000   500.000000  1600.000000   \n",
       "25%        97.090517    64.000000   128.000000  1000.000000  1600.000000   \n",
       "50%        97.149295    64.000000   160.000000  1500.000000  1600.000000   \n",
       "75%        97.217868    96.000000   192.000000  2000.000000  2400.000000   \n",
       "max        97.531348    96.000000   224.000000  2000.000000  2400.000000   \n",
       "\n",
       "            l3_dim  l1_wt_sparsity  l2_wt_sparsity  l3_wt_sparsity  \\\n",
       "count   170.000000           170.0      170.000000      170.000000   \n",
       "mean   4037.647059             0.0        0.879941        0.967294   \n",
       "std    1032.716470             0.0        0.059251        0.015737   \n",
       "min    1600.000000             0.0        0.800000        0.950000   \n",
       "25%    3200.000000             0.0        0.800000        0.950000   \n",
       "50%    4000.000000             0.0        0.900000        0.975000   \n",
       "75%    4800.000000             0.0        0.900000        0.980000   \n",
       "max    5600.000000             0.0        0.980000        0.995000   \n",
       "\n",
       "       l2_wts_per_kernel  l3_wts_per_unit   dimensions  non_zero_params  \\\n",
       "count         170.000000       170.000000   170.000000       170.000000   \n",
       "mean          209.223529       130.894118  2286.818599    221405.929412   \n",
       "std            90.258324        72.460072   451.023283    100271.520471   \n",
       "min            32.000000        16.000000  1341.640786     80220.000000   \n",
       "25%           160.000000        80.000000  1897.366596    149512.000000   \n",
       "50%           160.000000       120.000000  2190.890230    196850.000000   \n",
       "75%           320.000000       200.000000  2683.281573    269481.000000   \n",
       "max           320.000000       280.000000  3098.386677    569308.000000   \n",
       "\n",
       "       num_trials  \n",
       "count       170.0  \n",
       "mean          4.0  \n",
       "std           0.0  \n",
       "min           4.0  \n",
       "25%           4.0  \n",
       "50%           4.0  \n",
       "75%           4.0  \n",
       "max           4.0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_wts_accurate.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_wts_accurate_pareto = sparse_wts_accurate.groupby(\"dimensions\").agg(\n",
    "        accuracy=('mean_accuracy', \"max\"),\n",
    "        non_zero_params=('non_zero_params', \"min\"),\n",
    "        dimensionality=('dimensions', \"first\"),\n",
    "        l3_wts_per_unit=(\"l3_wts_per_unit\", \"min\"),\n",
    "        l2_wts_per_kernel=(\"l2_wts_per_kernel\", \"min\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_activations_accurate = sparse_activations_id[sparse_activations_id[\"mean_accuracy\"] >= best_dense_accuracy]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_accuracy</th>\n",
       "      <th>l1_channels</th>\n",
       "      <th>l2_channels</th>\n",
       "      <th>l3_n</th>\n",
       "      <th>l2_dim</th>\n",
       "      <th>l3_dim</th>\n",
       "      <th>l1_wt_sparsity</th>\n",
       "      <th>l2_wt_sparsity</th>\n",
       "      <th>l3_wt_sparsity</th>\n",
       "      <th>l2_wts_per_kernel</th>\n",
       "      <th>l3_wts_per_unit</th>\n",
       "      <th>dimensions</th>\n",
       "      <th>non_zero_params</th>\n",
       "      <th>num_trials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.0</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>97.193325</td>\n",
       "      <td>66.408602</td>\n",
       "      <td>120.430108</td>\n",
       "      <td>1548.387097</td>\n",
       "      <td>1660.215054</td>\n",
       "      <td>3010.752688</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.877742</td>\n",
       "      <td>0.969785</td>\n",
       "      <td>198.365591</td>\n",
       "      <td>86.107527</td>\n",
       "      <td>2102.922049</td>\n",
       "      <td>172431.526882</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.125000</td>\n",
       "      <td>8.488146</td>\n",
       "      <td>44.610244</td>\n",
       "      <td>448.817989</td>\n",
       "      <td>212.203648</td>\n",
       "      <td>1115.256099</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.068749</td>\n",
       "      <td>0.014889</td>\n",
       "      <td>107.419109</td>\n",
       "      <td>42.641443</td>\n",
       "      <td>512.112562</td>\n",
       "      <td>73254.973826</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>97.031740</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>750.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1095.445115</td>\n",
       "      <td>64220.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>97.100313</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1788.854382</td>\n",
       "      <td>115220.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>97.159091</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>3200.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>2190.890230</td>\n",
       "      <td>157496.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>97.257053</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>3200.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>2529.822128</td>\n",
       "      <td>207320.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>97.521552</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>5600.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.995000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>3346.640106</td>\n",
       "      <td>388764.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean_accuracy  l1_channels  l2_channels         l3_n       l2_dim  \\\n",
       "count      93.000000    93.000000    93.000000    93.000000    93.000000   \n",
       "mean       97.193325    66.408602   120.430108  1548.387097  1660.215054   \n",
       "std         0.125000     8.488146    44.610244   448.817989   212.203648   \n",
       "min        97.031740    64.000000    64.000000   750.000000  1600.000000   \n",
       "25%        97.100313    64.000000    96.000000  1000.000000  1600.000000   \n",
       "50%        97.159091    64.000000   128.000000  1500.000000  1600.000000   \n",
       "75%        97.257053    64.000000   128.000000  2000.000000  1600.000000   \n",
       "max        97.521552    96.000000   224.000000  2000.000000  2400.000000   \n",
       "\n",
       "            l3_dim  l1_wt_sparsity  l2_wt_sparsity  l3_wt_sparsity  \\\n",
       "count    93.000000            93.0       93.000000       93.000000   \n",
       "mean   3010.752688             0.0        0.877742        0.969785   \n",
       "std    1115.256099             0.0        0.068749        0.014889   \n",
       "min    1600.000000             0.0        0.800000        0.950000   \n",
       "25%    2400.000000             0.0        0.800000        0.950000   \n",
       "50%    3200.000000             0.0        0.900000        0.975000   \n",
       "75%    3200.000000             0.0        0.950000        0.980000   \n",
       "max    5600.000000             0.0        0.980000        0.995000   \n",
       "\n",
       "       l2_wts_per_kernel  l3_wts_per_unit   dimensions  non_zero_params  \\\n",
       "count          93.000000        93.000000    93.000000        93.000000   \n",
       "mean          198.365591        86.107527  2102.922049    172431.526882   \n",
       "std           107.419109        42.641443   512.112562     73254.973826   \n",
       "min            32.000000         8.000000  1095.445115     64220.000000   \n",
       "25%            80.000000        60.000000  1788.854382    115220.000000   \n",
       "50%           160.000000        80.000000  2190.890230    157496.000000   \n",
       "75%           320.000000       120.000000  2529.822128    207320.000000   \n",
       "max           320.000000       160.000000  3346.640106    388764.000000   \n",
       "\n",
       "       num_trials  \n",
       "count        93.0  \n",
       "mean          4.0  \n",
       "std           0.0  \n",
       "min           4.0  \n",
       "25%           4.0  \n",
       "50%           4.0  \n",
       "75%           4.0  \n",
       "max           4.0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_activations_accurate.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_activations_accurate_pareto = sparse_activations_accurate.groupby(\"dimensions\").agg(\n",
    "        accuracy=('mean_accuracy', \"max\"),\n",
    "        non_zero_params=('non_zero_params', \"min\"),\n",
    "        dimensionality=('dimensions', \"first\"),\n",
    "        l3_wts_per_unit=(\"l3_wts_per_unit\", \"min\"),\n",
    "        l2_wts_per_kernel=(\"l2_wts_per_kernel\", \"min\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(dpi=125)\n",
    "plt.title(\"Weights per neuron in linear layer (good networks only)\")\n",
    "plt.xlabel(\"Dimensionality\")\n",
    "plt.ylabel(\"Weights per unit\")\n",
    "plt.scatter(sparse_wts_accurate_pareto[\"dimensionality\"], sparse_wts_accurate_pareto[\"l3_wts_per_unit\"], label=\"Sparse weights\", s=10)\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.savefig(\"plots/l3_wts_per_neuron.png\", dpi=300)\n",
    "plt.scatter(sparse_activations_accurate_pareto[\"dimensionality\"], sparse_activations_accurate_pareto[\"l3_wts_per_unit\"], label=\"Sparse weights+activations\", s=10)\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.savefig(\"plots/l3_wts_per_neuron_all.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(dpi=125)\n",
    "plt.title(\"Weights per neuron in CNN2 layer (good networks only)\")\n",
    "plt.xlabel(\"Dimensionality\")\n",
    "plt.ylabel(\"Weights per unit\")\n",
    "plt.scatter(sparse_wts_accurate_pareto[\"dimensionality\"], sparse_wts_accurate_pareto[\"l2_wts_per_kernel\"], label=\"Sparse weights\", s=10)\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.savefig(\"plots/l2_wts_per_neuron.png\", dpi=300)\n",
    "plt.scatter(sparse_activations_accurate_pareto[\"dimensionality\"], sparse_activations_accurate_pareto[\"l2_wts_per_kernel\"], label=\"Sparse weights+activations\", s=10)\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.savefig(\"plots/l2_wts_per_neuron_all.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataframe containing highly accurate small networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_accuracy</th>\n",
       "      <th>l1_channels</th>\n",
       "      <th>l2_channels</th>\n",
       "      <th>l3_n</th>\n",
       "      <th>l2_dim</th>\n",
       "      <th>l3_dim</th>\n",
       "      <th>l1_wt_sparsity</th>\n",
       "      <th>l2_wt_sparsity</th>\n",
       "      <th>l3_wt_sparsity</th>\n",
       "      <th>l2_wts_per_kernel</th>\n",
       "      <th>l3_wts_per_unit</th>\n",
       "      <th>dimensions</th>\n",
       "      <th>non_zero_params</th>\n",
       "      <th>config</th>\n",
       "      <th>num_trials</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>config</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sparse_Baselines2 cnn_out_channels=(64 192)cnn_weight_sparsity=(0.0 0.2)linear_n=(1000)weight_sparsity=(0.05)</th>\n",
       "      <td>97.501959</td>\n",
       "      <td>64</td>\n",
       "      <td>192</td>\n",
       "      <td>1000</td>\n",
       "      <td>1600</td>\n",
       "      <td>4800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.95</td>\n",
       "      <td>320.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>2190.890230</td>\n",
       "      <td>316308</td>\n",
       "      <td>Sparse_Baselines2 cnn_out_channels=(64 192)cnn...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sparse_Baselines4 cnn_out_channels=(96 160)cnn_weight_sparsity=(0.0 0.1)linear_n=(1500)weight_sparsity=(0.05)</th>\n",
       "      <td>97.531348</td>\n",
       "      <td>96</td>\n",
       "      <td>160</td>\n",
       "      <td>1500</td>\n",
       "      <td>2400</td>\n",
       "      <td>4000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.95</td>\n",
       "      <td>240.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>2449.489743</td>\n",
       "      <td>360568</td>\n",
       "      <td>Sparse_Baselines4 cnn_out_channels=(96 160)cnn...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    mean_accuracy  \\\n",
       "config                                                              \n",
       "Sparse_Baselines2 cnn_out_channels=(64 192)cnn_...      97.501959   \n",
       "Sparse_Baselines4 cnn_out_channels=(96 160)cnn_...      97.531348   \n",
       "\n",
       "                                                    l1_channels  l2_channels  \\\n",
       "config                                                                         \n",
       "Sparse_Baselines2 cnn_out_channels=(64 192)cnn_...           64          192   \n",
       "Sparse_Baselines4 cnn_out_channels=(96 160)cnn_...           96          160   \n",
       "\n",
       "                                                    l3_n  l2_dim  l3_dim  \\\n",
       "config                                                                     \n",
       "Sparse_Baselines2 cnn_out_channels=(64 192)cnn_...  1000    1600    4800   \n",
       "Sparse_Baselines4 cnn_out_channels=(96 160)cnn_...  1500    2400    4000   \n",
       "\n",
       "                                                    l1_wt_sparsity  \\\n",
       "config                                                               \n",
       "Sparse_Baselines2 cnn_out_channels=(64 192)cnn_...             0.0   \n",
       "Sparse_Baselines4 cnn_out_channels=(96 160)cnn_...             0.0   \n",
       "\n",
       "                                                    l2_wt_sparsity  \\\n",
       "config                                                               \n",
       "Sparse_Baselines2 cnn_out_channels=(64 192)cnn_...             0.8   \n",
       "Sparse_Baselines4 cnn_out_channels=(96 160)cnn_...             0.9   \n",
       "\n",
       "                                                    l3_wt_sparsity  \\\n",
       "config                                                               \n",
       "Sparse_Baselines2 cnn_out_channels=(64 192)cnn_...            0.95   \n",
       "Sparse_Baselines4 cnn_out_channels=(96 160)cnn_...            0.95   \n",
       "\n",
       "                                                    l2_wts_per_kernel  \\\n",
       "config                                                                  \n",
       "Sparse_Baselines2 cnn_out_channels=(64 192)cnn_...              320.0   \n",
       "Sparse_Baselines4 cnn_out_channels=(96 160)cnn_...              240.0   \n",
       "\n",
       "                                                    l3_wts_per_unit  \\\n",
       "config                                                                \n",
       "Sparse_Baselines2 cnn_out_channels=(64 192)cnn_...            240.0   \n",
       "Sparse_Baselines4 cnn_out_channels=(96 160)cnn_...            200.0   \n",
       "\n",
       "                                                     dimensions  \\\n",
       "config                                                            \n",
       "Sparse_Baselines2 cnn_out_channels=(64 192)cnn_...  2190.890230   \n",
       "Sparse_Baselines4 cnn_out_channels=(96 160)cnn_...  2449.489743   \n",
       "\n",
       "                                                    non_zero_params  \\\n",
       "config                                                                \n",
       "Sparse_Baselines2 cnn_out_channels=(64 192)cnn_...           316308   \n",
       "Sparse_Baselines4 cnn_out_channels=(96 160)cnn_...           360568   \n",
       "\n",
       "                                                                                               config  \\\n",
       "config                                                                                                  \n",
       "Sparse_Baselines2 cnn_out_channels=(64 192)cnn_...  Sparse_Baselines2 cnn_out_channels=(64 192)cnn...   \n",
       "Sparse_Baselines4 cnn_out_channels=(96 160)cnn_...  Sparse_Baselines4 cnn_out_channels=(96 160)cnn...   \n",
       "\n",
       "                                                    num_trials  \n",
       "config                                                          \n",
       "Sparse_Baselines2 cnn_out_channels=(64 192)cnn_...           4  \n",
       "Sparse_Baselines4 cnn_out_channels=(96 160)cnn_...           4  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_wts_id[(sparse_wts_id[\"mean_accuracy\"] >= 97.5) & (sparse_wts_id[\"non_zero_params\"]<2000000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_accuracy</th>\n",
       "      <th>l1_channels</th>\n",
       "      <th>l2_channels</th>\n",
       "      <th>l3_n</th>\n",
       "      <th>l2_dim</th>\n",
       "      <th>l3_dim</th>\n",
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       "      <th>l2_wt_sparsity</th>\n",
       "      <th>l3_wt_sparsity</th>\n",
       "      <th>l2_wts_per_kernel</th>\n",
       "      <th>l3_wts_per_unit</th>\n",
       "      <th>dimensions</th>\n",
       "      <th>non_zero_params</th>\n",
       "      <th>config</th>\n",
       "      <th>num_trials</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>config</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kwinner_Sparse_Baselines cnn_out_channels=(64 128)cnn_weight_sparsity=(0.0 0.1)linear_n=(1000)weight_sparsity=(0.02)</th>\n",
       "      <td>97.227665</td>\n",
       "      <td>64</td>\n",
       "      <td>128</td>\n",
       "      <td>1000</td>\n",
       "      <td>1600</td>\n",
       "      <td>3200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.980</td>\n",
       "      <td>160.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1788.854382</td>\n",
       "      <td>99284</td>\n",
       "      <td>Kwinner_Sparse_Baselines cnn_out_channels=(64 ...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kwinner_Sparse_Baselines cnn_out_channels=(64 64)cnn_weight_sparsity=(0.0 0.2)linear_n=(1000)weight_sparsity=(0.025)</th>\n",
       "      <td>97.080721</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>1000</td>\n",
       "      <td>1600</td>\n",
       "      <td>1600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.975</td>\n",
       "      <td>320.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1264.911064</td>\n",
       "      <td>75220</td>\n",
       "      <td>Kwinner_Sparse_Baselines cnn_out_channels=(64 ...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kwinner_Sparse_Baselines cnn_out_channels=(64 64)cnn_weight_sparsity=(0.0 0.2)linear_n=(1500)weight_sparsity=(0.02)</th>\n",
       "      <td>97.051332</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>1500</td>\n",
       "      <td>1600</td>\n",
       "      <td>1600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.980</td>\n",
       "      <td>320.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1549.193338</td>\n",
       "      <td>89720</td>\n",
       "      <td>Kwinner_Sparse_Baselines cnn_out_channels=(64 ...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kwinner_Sparse_Baselines cnn_out_channels=(64 64)cnn_weight_sparsity=(0.0 0.2)linear_n=(2000)weight_sparsity=(0.005)</th>\n",
       "      <td>97.041536</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>2000</td>\n",
       "      <td>1600</td>\n",
       "      <td>1600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.995</td>\n",
       "      <td>320.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1788.854382</td>\n",
       "      <td>64220</td>\n",
       "      <td>Kwinner_Sparse_Baselines cnn_out_channels=(64 ...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kwinner_Sparse_Baselines cnn_out_channels=(64 64)cnn_weight_sparsity=(0.0 0.2)linear_n=(750)weight_sparsity=(0.05)</th>\n",
       "      <td>97.080721</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>750</td>\n",
       "      <td>1600</td>\n",
       "      <td>1600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.950</td>\n",
       "      <td>320.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1095.445115</td>\n",
       "      <td>91970</td>\n",
       "      <td>Kwinner_Sparse_Baselines cnn_out_channels=(64 ...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kwinner_Sparse_Baselines cnn_out_channels=(64 96)cnn_weight_sparsity=(0.0 0.1)linear_n=(2000)weight_sparsity=(0.01)</th>\n",
       "      <td>97.100313</td>\n",
       "      <td>64</td>\n",
       "      <td>96</td>\n",
       "      <td>2000</td>\n",
       "      <td>1600</td>\n",
       "      <td>2400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.990</td>\n",
       "      <td>160.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2190.890230</td>\n",
       "      <td>91132</td>\n",
       "      <td>Kwinner_Sparse_Baselines cnn_out_channels=(64 ...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kwinner_Sparse_Baselines cnn_out_channels=(64 96)cnn_weight_sparsity=(0.0 0.2)linear_n=(1000)weight_sparsity=(0.02)</th>\n",
       "      <td>97.080721</td>\n",
       "      <td>64</td>\n",
       "      <td>96</td>\n",
       "      <td>1000</td>\n",
       "      <td>1600</td>\n",
       "      <td>2400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.980</td>\n",
       "      <td>320.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1549.193338</td>\n",
       "      <td>93492</td>\n",
       "      <td>Kwinner_Sparse_Baselines cnn_out_channels=(64 ...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kwinner_Sparse_Baselines cnn_out_channels=(64 96)cnn_weight_sparsity=(0.0 0.2)linear_n=(2000)weight_sparsity=(0.005)</th>\n",
       "      <td>97.041536</td>\n",
       "      <td>64</td>\n",
       "      <td>96</td>\n",
       "      <td>2000</td>\n",
       "      <td>1600</td>\n",
       "      <td>2400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.995</td>\n",
       "      <td>320.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2190.890230</td>\n",
       "      <td>82492</td>\n",
       "      <td>Kwinner_Sparse_Baselines cnn_out_channels=(64 ...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kwinner_Sparse_Baselines cnn_out_channels=(64 96)cnn_weight_sparsity=(0.0 0.2)linear_n=(750)weight_sparsity=(0.025)</th>\n",
       "      <td>97.041536</td>\n",
       "      <td>64</td>\n",
       "      <td>96</td>\n",
       "      <td>750</td>\n",
       "      <td>1600</td>\n",
       "      <td>2400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.975</td>\n",
       "      <td>320.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1341.640786</td>\n",
       "      <td>87242</td>\n",
       "      <td>Kwinner_Sparse_Baselines cnn_out_channels=(64 ...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    mean_accuracy  \\\n",
       "config                                                              \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 1...      97.227665   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...      97.080721   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...      97.051332   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...      97.041536   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...      97.080721   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...      97.100313   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...      97.080721   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...      97.041536   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...      97.041536   \n",
       "\n",
       "                                                    l1_channels  l2_channels  \\\n",
       "config                                                                         \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 1...           64          128   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...           64           64   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...           64           64   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...           64           64   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...           64           64   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...           64           96   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...           64           96   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...           64           96   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...           64           96   \n",
       "\n",
       "                                                    l3_n  l2_dim  l3_dim  \\\n",
       "config                                                                     \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 1...  1000    1600    3200   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...  1000    1600    1600   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...  1500    1600    1600   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...  2000    1600    1600   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...   750    1600    1600   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...  2000    1600    2400   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...  1000    1600    2400   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...  2000    1600    2400   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...   750    1600    2400   \n",
       "\n",
       "                                                    l1_wt_sparsity  \\\n",
       "config                                                               \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 1...             0.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...             0.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...             0.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...             0.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...             0.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...             0.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...             0.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...             0.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...             0.0   \n",
       "\n",
       "                                                    l2_wt_sparsity  \\\n",
       "config                                                               \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 1...             0.9   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...             0.8   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...             0.8   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...             0.8   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...             0.8   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...             0.9   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...             0.8   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...             0.8   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...             0.8   \n",
       "\n",
       "                                                    l3_wt_sparsity  \\\n",
       "config                                                               \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 1...           0.980   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...           0.975   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...           0.980   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...           0.995   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...           0.950   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...           0.990   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...           0.980   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...           0.995   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...           0.975   \n",
       "\n",
       "                                                    l2_wts_per_kernel  \\\n",
       "config                                                                  \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 1...              160.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...              320.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...              320.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...              320.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...              320.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...              160.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...              320.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...              320.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...              320.0   \n",
       "\n",
       "                                                    l3_wts_per_unit  \\\n",
       "config                                                                \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 1...             64.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...             40.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...             32.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...              8.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...             80.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...             24.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...             48.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...             12.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...             60.0   \n",
       "\n",
       "                                                     dimensions  \\\n",
       "config                                                            \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 1...  1788.854382   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...  1264.911064   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...  1549.193338   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...  1788.854382   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...  1095.445115   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...  2190.890230   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...  1549.193338   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...  2190.890230   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...  1341.640786   \n",
       "\n",
       "                                                    non_zero_params  \\\n",
       "config                                                                \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 1...            99284   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...            75220   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...            89720   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...            64220   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...            91970   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...            91132   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...            93492   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...            82492   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...            87242   \n",
       "\n",
       "                                                                                               config  \\\n",
       "config                                                                                                  \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 1...  Kwinner_Sparse_Baselines cnn_out_channels=(64 ...   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...  Kwinner_Sparse_Baselines cnn_out_channels=(64 ...   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...  Kwinner_Sparse_Baselines cnn_out_channels=(64 ...   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...  Kwinner_Sparse_Baselines cnn_out_channels=(64 ...   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...  Kwinner_Sparse_Baselines cnn_out_channels=(64 ...   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...  Kwinner_Sparse_Baselines cnn_out_channels=(64 ...   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...  Kwinner_Sparse_Baselines cnn_out_channels=(64 ...   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...  Kwinner_Sparse_Baselines cnn_out_channels=(64 ...   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...  Kwinner_Sparse_Baselines cnn_out_channels=(64 ...   \n",
       "\n",
       "                                                    num_trials  \n",
       "config                                                          \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 1...           4  \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...           4  \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...           4  \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...           4  \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...           4  \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...           4  \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...           4  \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...           4  \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...           4  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_activations_id[(sparse_activations_id[\"mean_accuracy\"] >= 97.03) & (sparse_activations_id[\"non_zero_params\"]<100000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_accuracy</th>\n",
       "      <th>l1_channels</th>\n",
       "      <th>l2_channels</th>\n",
       "      <th>l3_n</th>\n",
       "      <th>l2_dim</th>\n",
       "      <th>l3_dim</th>\n",
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       "      <th>l2_wts_per_kernel</th>\n",
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       "      <th>dimensions</th>\n",
       "      <th>non_zero_params</th>\n",
       "      <th>num_trials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.00000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>96.971563</td>\n",
       "      <td>64.0</td>\n",
       "      <td>100.571429</td>\n",
       "      <td>1500.00000</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>2514.285714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>2514.285714</td>\n",
       "      <td>1891.780289</td>\n",
       "      <td>3.953619e+06</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.041341</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.209439</td>\n",
       "      <td>408.24829</td>\n",
       "      <td>0.0</td>\n",
       "      <td>855.235974</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>855.235974</td>\n",
       "      <td>474.020284</td>\n",
       "      <td>1.770517e+06</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>96.904389</td>\n",
       "      <td>64.0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>1000.00000</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>1095.445115</td>\n",
       "      <td>1.272408e+06</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>96.953370</td>\n",
       "      <td>64.0</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>1250.00000</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>1669.023860</td>\n",
       "      <td>2.993988e+06</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>96.963166</td>\n",
       "      <td>64.0</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>1500.00000</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>1897.366596</td>\n",
       "      <td>3.774872e+06</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>96.997453</td>\n",
       "      <td>64.0</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>1750.00000</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>3200.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>3200.000000</td>\n",
       "      <td>2190.890230</td>\n",
       "      <td>5.003738e+06</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>97.031740</td>\n",
       "      <td>64.0</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>2000.00000</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>3200.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>3200.000000</td>\n",
       "      <td>2529.822128</td>\n",
       "      <td>6.632604e+06</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean_accuracy  l1_channels  l2_channels        l3_n  l2_dim  \\\n",
       "count       7.000000          7.0     7.000000     7.00000     7.0   \n",
       "mean       96.971563         64.0   100.571429  1500.00000  1600.0   \n",
       "std         0.041341          0.0    34.209439   408.24829     0.0   \n",
       "min        96.904389         64.0    32.000000  1000.00000  1600.0   \n",
       "25%        96.953370         64.0    96.000000  1250.00000  1600.0   \n",
       "50%        96.963166         64.0    96.000000  1500.00000  1600.0   \n",
       "75%        96.997453         64.0   128.000000  1750.00000  1600.0   \n",
       "max        97.031740         64.0   128.000000  2000.00000  1600.0   \n",
       "\n",
       "            l3_dim  l1_wt_sparsity  l2_wt_sparsity  l3_wt_sparsity  \\\n",
       "count     7.000000             7.0             7.0             7.0   \n",
       "mean   2514.285714             0.0             0.0             0.0   \n",
       "std     855.235974             0.0             0.0             0.0   \n",
       "min     800.000000             0.0             0.0             0.0   \n",
       "25%    2400.000000             0.0             0.0             0.0   \n",
       "50%    2400.000000             0.0             0.0             0.0   \n",
       "75%    3200.000000             0.0             0.0             0.0   \n",
       "max    3200.000000             0.0             0.0             0.0   \n",
       "\n",
       "       l2_wts_per_kernel  l3_wts_per_unit   dimensions  non_zero_params  \\\n",
       "count                7.0         7.000000     7.000000     7.000000e+00   \n",
       "mean              1600.0      2514.285714  1891.780289     3.953619e+06   \n",
       "std                  0.0       855.235974   474.020284     1.770517e+06   \n",
       "min               1600.0       800.000000  1095.445115     1.272408e+06   \n",
       "25%               1600.0      2400.000000  1669.023860     2.993988e+06   \n",
       "50%               1600.0      2400.000000  1897.366596     3.774872e+06   \n",
       "75%               1600.0      3200.000000  2190.890230     5.003738e+06   \n",
       "max               1600.0      3200.000000  2529.822128     6.632604e+06   \n",
       "\n",
       "       num_trials  \n",
       "count         7.0  \n",
       "mean          4.0  \n",
       "std           0.0  \n",
       "min           4.0  \n",
       "25%           4.0  \n",
       "50%           4.0  \n",
       "75%           4.0  \n",
       "max           4.0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dense_id[(dense_id[\"mean_accuracy\"] >= 96.9)].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reasonably accurate but really small networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>mean_accuracy</th>\n",
       "      <th>l1_channels</th>\n",
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       "      <th>config</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sparse_Baselines cnn_out_channels=(64 128)cnn_weight_sparsity=(0.0 0.1)linear_n=(1000)weight_sparsity=(0.02)</th>\n",
       "      <td>97.070925</td>\n",
       "      <td>64</td>\n",
       "      <td>128</td>\n",
       "      <td>1000</td>\n",
       "      <td>1600</td>\n",
       "      <td>3200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.98</td>\n",
       "      <td>160.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1788.854382</td>\n",
       "      <td>99284</td>\n",
       "      <td>Sparse_Baselines cnn_out_channels=(64 128)cnn_...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sparse_Baselines cnn_out_channels=(64 64)cnn_weight_sparsity=(0.0 0.2)linear_n=(2000)weight_sparsity=(0.01)</th>\n",
       "      <td>97.119906</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>2000</td>\n",
       "      <td>1600</td>\n",
       "      <td>1600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.99</td>\n",
       "      <td>320.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1788.854382</td>\n",
       "      <td>80220</td>\n",
       "      <td>Sparse_Baselines cnn_out_channels=(64 64)cnn_w...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sparse_Baselines3 cnn_out_channels=(96 128)cnn_weight_sparsity=(0.0 0.1)linear_n=(750)weight_sparsity=(0.02)</th>\n",
       "      <td>97.090517</td>\n",
       "      <td>96</td>\n",
       "      <td>128</td>\n",
       "      <td>750</td>\n",
       "      <td>2400</td>\n",
       "      <td>3200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.98</td>\n",
       "      <td>240.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1549.193338</td>\n",
       "      <td>91106</td>\n",
       "      <td>Sparse_Baselines3 cnn_out_channels=(96 128)cnn...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sparse_Baselines4 cnn_out_channels=(96 192)cnn_weight_sparsity=(0.0 0.1)linear_n=(750)weight_sparsity=(0.01)</th>\n",
       "      <td>97.159091</td>\n",
       "      <td>96</td>\n",
       "      <td>192</td>\n",
       "      <td>750</td>\n",
       "      <td>2400</td>\n",
       "      <td>4800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.99</td>\n",
       "      <td>240.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1897.366596</td>\n",
       "      <td>94530</td>\n",
       "      <td>Sparse_Baselines4 cnn_out_channels=(96 192)cnn...</td>\n",
       "      <td>4</td>\n",
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       "</table>\n",
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      "text/plain": [
       "                                                    mean_accuracy  \\\n",
       "config                                                              \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...      97.070925   \n",
       "Sparse_Baselines cnn_out_channels=(64 64)cnn_we...      97.119906   \n",
       "Sparse_Baselines3 cnn_out_channels=(96 128)cnn_...      97.090517   \n",
       "Sparse_Baselines4 cnn_out_channels=(96 192)cnn_...      97.159091   \n",
       "\n",
       "                                                    l1_channels  l2_channels  \\\n",
       "config                                                                         \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...           64          128   \n",
       "Sparse_Baselines cnn_out_channels=(64 64)cnn_we...           64           64   \n",
       "Sparse_Baselines3 cnn_out_channels=(96 128)cnn_...           96          128   \n",
       "Sparse_Baselines4 cnn_out_channels=(96 192)cnn_...           96          192   \n",
       "\n",
       "                                                    l3_n  l2_dim  l3_dim  \\\n",
       "config                                                                     \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...  1000    1600    3200   \n",
       "Sparse_Baselines cnn_out_channels=(64 64)cnn_we...  2000    1600    1600   \n",
       "Sparse_Baselines3 cnn_out_channels=(96 128)cnn_...   750    2400    3200   \n",
       "Sparse_Baselines4 cnn_out_channels=(96 192)cnn_...   750    2400    4800   \n",
       "\n",
       "                                                    l1_wt_sparsity  \\\n",
       "config                                                               \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...             0.0   \n",
       "Sparse_Baselines cnn_out_channels=(64 64)cnn_we...             0.0   \n",
       "Sparse_Baselines3 cnn_out_channels=(96 128)cnn_...             0.0   \n",
       "Sparse_Baselines4 cnn_out_channels=(96 192)cnn_...             0.0   \n",
       "\n",
       "                                                    l2_wt_sparsity  \\\n",
       "config                                                               \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...             0.9   \n",
       "Sparse_Baselines cnn_out_channels=(64 64)cnn_we...             0.8   \n",
       "Sparse_Baselines3 cnn_out_channels=(96 128)cnn_...             0.9   \n",
       "Sparse_Baselines4 cnn_out_channels=(96 192)cnn_...             0.9   \n",
       "\n",
       "                                                    l3_wt_sparsity  \\\n",
       "config                                                               \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...            0.98   \n",
       "Sparse_Baselines cnn_out_channels=(64 64)cnn_we...            0.99   \n",
       "Sparse_Baselines3 cnn_out_channels=(96 128)cnn_...            0.98   \n",
       "Sparse_Baselines4 cnn_out_channels=(96 192)cnn_...            0.99   \n",
       "\n",
       "                                                    l2_wts_per_kernel  \\\n",
       "config                                                                  \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...              160.0   \n",
       "Sparse_Baselines cnn_out_channels=(64 64)cnn_we...              320.0   \n",
       "Sparse_Baselines3 cnn_out_channels=(96 128)cnn_...              240.0   \n",
       "Sparse_Baselines4 cnn_out_channels=(96 192)cnn_...              240.0   \n",
       "\n",
       "                                                    l3_wts_per_unit  \\\n",
       "config                                                                \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...             64.0   \n",
       "Sparse_Baselines cnn_out_channels=(64 64)cnn_we...             16.0   \n",
       "Sparse_Baselines3 cnn_out_channels=(96 128)cnn_...             64.0   \n",
       "Sparse_Baselines4 cnn_out_channels=(96 192)cnn_...             48.0   \n",
       "\n",
       "                                                     dimensions  \\\n",
       "config                                                            \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...  1788.854382   \n",
       "Sparse_Baselines cnn_out_channels=(64 64)cnn_we...  1788.854382   \n",
       "Sparse_Baselines3 cnn_out_channels=(96 128)cnn_...  1549.193338   \n",
       "Sparse_Baselines4 cnn_out_channels=(96 192)cnn_...  1897.366596   \n",
       "\n",
       "                                                    non_zero_params  \\\n",
       "config                                                                \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...            99284   \n",
       "Sparse_Baselines cnn_out_channels=(64 64)cnn_we...            80220   \n",
       "Sparse_Baselines3 cnn_out_channels=(96 128)cnn_...            91106   \n",
       "Sparse_Baselines4 cnn_out_channels=(96 192)cnn_...            94530   \n",
       "\n",
       "                                                                                               config  \\\n",
       "config                                                                                                  \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...  Sparse_Baselines cnn_out_channels=(64 128)cnn_...   \n",
       "Sparse_Baselines cnn_out_channels=(64 64)cnn_we...  Sparse_Baselines cnn_out_channels=(64 64)cnn_w...   \n",
       "Sparse_Baselines3 cnn_out_channels=(96 128)cnn_...  Sparse_Baselines3 cnn_out_channels=(96 128)cnn...   \n",
       "Sparse_Baselines4 cnn_out_channels=(96 192)cnn_...  Sparse_Baselines4 cnn_out_channels=(96 192)cnn...   \n",
       "\n",
       "                                                    num_trials  \n",
       "config                                                          \n",
       "Sparse_Baselines cnn_out_channels=(64 128)cnn_w...           4  \n",
       "Sparse_Baselines cnn_out_channels=(64 64)cnn_we...           4  \n",
       "Sparse_Baselines3 cnn_out_channels=(96 128)cnn_...           4  \n",
       "Sparse_Baselines4 cnn_out_channels=(96 192)cnn_...           4  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_wts_id[(sparse_wts_id[\"mean_accuracy\"] >= best_dense_accuracy) & (sparse_wts_id[\"non_zero_params\"]<100000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_accuracy</th>\n",
       "      <th>l1_channels</th>\n",
       "      <th>l2_channels</th>\n",
       "      <th>l3_n</th>\n",
       "      <th>l2_dim</th>\n",
       "      <th>l3_dim</th>\n",
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       "      <th>l2_wt_sparsity</th>\n",
       "      <th>l3_wt_sparsity</th>\n",
       "      <th>l2_wts_per_kernel</th>\n",
       "      <th>l3_wts_per_unit</th>\n",
       "      <th>dimensions</th>\n",
       "      <th>non_zero_params</th>\n",
       "      <th>config</th>\n",
       "      <th>num_trials</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>config</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kwinner_Sparse_Baselines cnn_out_channels=(64 64)cnn_weight_sparsity=(0.0 0.2)linear_n=(1000)weight_sparsity=(0.025)</th>\n",
       "      <td>97.080721</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>1000</td>\n",
       "      <td>1600</td>\n",
       "      <td>1600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.975</td>\n",
       "      <td>320.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1264.911064</td>\n",
       "      <td>75220</td>\n",
       "      <td>Kwinner_Sparse_Baselines cnn_out_channels=(64 ...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kwinner_Sparse_Baselines cnn_out_channels=(64 64)cnn_weight_sparsity=(0.0 0.2)linear_n=(1500)weight_sparsity=(0.02)</th>\n",
       "      <td>97.051332</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>1500</td>\n",
       "      <td>1600</td>\n",
       "      <td>1600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.980</td>\n",
       "      <td>320.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1549.193338</td>\n",
       "      <td>89720</td>\n",
       "      <td>Kwinner_Sparse_Baselines cnn_out_channels=(64 ...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kwinner_Sparse_Baselines cnn_out_channels=(64 64)cnn_weight_sparsity=(0.0 0.2)linear_n=(2000)weight_sparsity=(0.005)</th>\n",
       "      <td>97.041536</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>2000</td>\n",
       "      <td>1600</td>\n",
       "      <td>1600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.995</td>\n",
       "      <td>320.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1788.854382</td>\n",
       "      <td>64220</td>\n",
       "      <td>Kwinner_Sparse_Baselines cnn_out_channels=(64 ...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kwinner_Sparse_Baselines cnn_out_channels=(64 96)cnn_weight_sparsity=(0.0 0.2)linear_n=(2000)weight_sparsity=(0.005)</th>\n",
       "      <td>97.041536</td>\n",
       "      <td>64</td>\n",
       "      <td>96</td>\n",
       "      <td>2000</td>\n",
       "      <td>1600</td>\n",
       "      <td>2400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.995</td>\n",
       "      <td>320.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2190.890230</td>\n",
       "      <td>82492</td>\n",
       "      <td>Kwinner_Sparse_Baselines cnn_out_channels=(64 ...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kwinner_Sparse_Baselines cnn_out_channels=(64 96)cnn_weight_sparsity=(0.0 0.2)linear_n=(750)weight_sparsity=(0.025)</th>\n",
       "      <td>97.041536</td>\n",
       "      <td>64</td>\n",
       "      <td>96</td>\n",
       "      <td>750</td>\n",
       "      <td>1600</td>\n",
       "      <td>2400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.975</td>\n",
       "      <td>320.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1341.640786</td>\n",
       "      <td>87242</td>\n",
       "      <td>Kwinner_Sparse_Baselines cnn_out_channels=(64 ...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    mean_accuracy  \\\n",
       "config                                                              \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...      97.080721   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...      97.051332   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...      97.041536   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...      97.041536   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...      97.041536   \n",
       "\n",
       "                                                    l1_channels  l2_channels  \\\n",
       "config                                                                         \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...           64           64   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...           64           64   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...           64           64   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...           64           96   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...           64           96   \n",
       "\n",
       "                                                    l3_n  l2_dim  l3_dim  \\\n",
       "config                                                                     \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...  1000    1600    1600   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...  1500    1600    1600   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...  2000    1600    1600   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...  2000    1600    2400   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...   750    1600    2400   \n",
       "\n",
       "                                                    l1_wt_sparsity  \\\n",
       "config                                                               \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...             0.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...             0.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...             0.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...             0.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...             0.0   \n",
       "\n",
       "                                                    l2_wt_sparsity  \\\n",
       "config                                                               \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...             0.8   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...             0.8   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...             0.8   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...             0.8   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...             0.8   \n",
       "\n",
       "                                                    l3_wt_sparsity  \\\n",
       "config                                                               \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...           0.975   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...           0.980   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...           0.995   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...           0.995   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...           0.975   \n",
       "\n",
       "                                                    l2_wts_per_kernel  \\\n",
       "config                                                                  \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...              320.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...              320.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...              320.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...              320.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...              320.0   \n",
       "\n",
       "                                                    l3_wts_per_unit  \\\n",
       "config                                                                \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...             40.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...             32.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...              8.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...             12.0   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...             60.0   \n",
       "\n",
       "                                                     dimensions  \\\n",
       "config                                                            \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...  1264.911064   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...  1549.193338   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...  1788.854382   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...  2190.890230   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...  1341.640786   \n",
       "\n",
       "                                                    non_zero_params  \\\n",
       "config                                                                \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...            75220   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...            89720   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...            64220   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...            82492   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...            87242   \n",
       "\n",
       "                                                                                               config  \\\n",
       "config                                                                                                  \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...  Kwinner_Sparse_Baselines cnn_out_channels=(64 ...   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...  Kwinner_Sparse_Baselines cnn_out_channels=(64 ...   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...  Kwinner_Sparse_Baselines cnn_out_channels=(64 ...   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...  Kwinner_Sparse_Baselines cnn_out_channels=(64 ...   \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...  Kwinner_Sparse_Baselines cnn_out_channels=(64 ...   \n",
       "\n",
       "                                                    num_trials  \n",
       "config                                                          \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...           4  \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...           4  \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 6...           4  \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...           4  \n",
       "Kwinner_Sparse_Baselines cnn_out_channels=(64 9...           4  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_activations_id[(sparse_activations_id[\"mean_accuracy\"] >= best_dense_accuracy) & (sparse_activations_id[\"non_zero_params\"]<90000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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